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atharvas/nltk_movies_emolex.ipynb

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nltk_movies_EmoLex.ipynb
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nltk_movies_emolex.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "nltk_movies_EmoLex.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [
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"toc_visible": true,
"include_colab_link": true
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"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/atharvas/93444379b8a205e19bc1edded53f90e5/nltk_movies_emolex.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uex_vcqtfdFh",
"colab_type": "text"
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XTNQpr1FdpCz",
"colab_type": "code",
"colab": {}
},
"source": [
"# general imports\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import re\n",
"import time \n",
"from tqdm import tqdm_notebook as tqdm\n",
"import string\n",
"from collections import defaultdict\n",
"import nltk\n",
"from nltk.metrics import precision, recall, f_measure\n",
"import glob\n",
"from sklearn.model_selection import KFold"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "EPAad7W32L_j",
"colab_type": "code",
"colab": {}
},
"source": [
"%%capture\n",
"nltk.download('punkt')\n",
"nltk.download('stopwords')\n",
"from nltk.corpus import stopwords\n",
"import string\n",
"from nltk.sentiment.util import mark_negation\n",
"from nltk.stem import WordNetLemmatizer \n",
"nltk.download('wordnet')\n",
"\n",
"# from nltk import ngrams\n",
"# from scipy.spatial.distance import cdist\n",
"# from collections import Counter\n",
"# from gensim.models import Word2Vec\n",
"# from sklearn import cluster"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "5Tk0B2fJffCW",
"colab_type": "text"
},
"source": [
"## Data Imports\n",
"The file path's here must be edited for the correct data to be pulled."
]
},
{
"cell_type": "code",
"metadata": {
"id": "YHXGKNRYfgOh",
"colab_type": "code",
"outputId": "07d797db-3cbe-4dba-b4dc-348761a2331d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 122
}
},
"source": [
"from google.colab import drive\n",
"drive.mount('/gdrive')\n",
"\n",
"base_path = \"/gdrive/My Drive/SPRING_2019/LING406/final_project/\"\n",
"raw_file_path = base_path + \"review_polarity/txt_sentoken/*/*.txt\"\n",
"emolex_path = base_path + \"NRC-Sentiment-Emotion-Lexicons/NRC-Sentiment-Emotion-Lexicons/NRC-Emotion-Lexicon-v0.92/NRC-Emotion-Lexicon-Wordlevel-v0.92.txt\""
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
"\n",
"Enter your authorization code:\n",
"··········\n",
"Mounted at /gdrive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_dDjAJdYfgy1",
"colab_type": "code",
"colab": {}
},
"source": [
"file_paths = []\n",
"for file in glob.glob(raw_file_path):\n",
" file_paths.append(file)\n",
" \n",
"small_paths = file_paths[0:10] + file_paths[1000:1010]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "EbFNnfew8NVJ",
"colab_type": "code",
"colab": {}
},
"source": [
"# # Adding punctuation marks to stopwords as well\n",
"stop_words = stopwords.words('english') + list(string.punctuation)\n",
"lemmatizer = WordNetLemmatizer() "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "y46wR08MimgV",
"colab_type": "text"
},
"source": [
"### Import & Clean Data\n",
"\n",
"Stratagies used:\n",
"1. Regex to extract words (no punctuation).\n",
"1. Regex to tokenize data.\n",
"1. No filtering of stopwords"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-4nzvLLiijtF",
"colab_type": "code",
"outputId": "8f991298-151c-48fe-cad0-3bd43b9c5724",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"def get_data(file_path):\n",
" '''\n",
" Helper function to read data from file and do basic preprocessing.\n",
" \n",
" inputs:\n",
" file_path : relative location of file.\n",
" \n",
" returns: \n",
" raw_data : string with no linebreaks. (makes regex easier)\n",
" '''\n",
" raw_data = str()\n",
" with open(file_path, 'r') as file:\n",
" raw_data = file.read()\n",
" return raw_data\n",
"\n",
"raw_data = get_data(file_paths[0])\n",
"raw_data[0:10]"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'plot : two'"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2UDMIiSoRVbM",
"colab_type": "code",
"outputId": "a3d1567e-e3b9-4c99-a650-a60f007cb72a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"def clean_data_with_stopwords(file_path):\n",
" '''\n",
" Helper function to read data from file and do basic preprocessing.\n",
" \n",
" inputs:\n",
" file_path : relative location of file.\n",
" \n",
" returns:\n",
" tuple consisting of:\n",
" label : whether emotion is positive (pos) or negative (neg)\n",
" cleaned_data : string after tokenization and taking out stop_words.\n",
" '''\n",
" label_pattern = re.compile('''\n",
" \\/(\\w{3})\\/.*\\.txt\n",
" ''', \n",
" re.IGNORECASE|re.VERBOSE)\n",
" review_pattern = re.compile('''\n",
" ([\\w]+)\n",
" ''', \n",
" re.IGNORECASE|re.VERBOSE)\n",
" \n",
" label = label_pattern.search(file_path).group(1)\n",
" \n",
" raw_data = get_data(file_path)\n",
" tokenized_data = review_pattern.findall(raw_data)\n",
" cleaned_data = [x for x in (tokenized_data) if x.replace(\"_NEG\", \"\") not in stop_words]\n",
" return label, cleaned_data\n",
"\n",
"sample_tuple = clean_data_with_stopwords(file_paths[0])\n",
"(sample_tuple[0], sample_tuple[1][5:10])"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('neg', ['church', 'party', 'drink', 'drive', 'get'])"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZXRfGW13EJkS",
"colab_type": "code",
"outputId": "e4582737-0090-4505-ca52-0783bd48edc3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"def clean_data_with_negation(file_path):\n",
" '''\n",
" Helper function to read data from file and do basic preprocessing.\n",
" \n",
" inputs:\n",
" file_path : relative location of file.\n",
" \n",
" returns:\n",
" tuple consisting of:\n",
" label : whether emotion is positive (pos) or negative (neg)\n",
" cleaned_data : string after tokenization and taking out stop_words.\n",
" '''\n",
" label_pattern = re.compile('''\n",
" \\/(\\w{3})\\/.*\\.txt\n",
" ''', \n",
" re.IGNORECASE|re.VERBOSE)\n",
" review_pattern = re.compile('''\n",
" ([\\w]+)\n",
" ''', \n",
" re.IGNORECASE|re.VERBOSE)\n",
" \n",
" label = label_pattern.search(file_path).group(1)\n",
" \n",
" raw_data = get_data(file_path)\n",
" tokenized_data = review_pattern.findall(raw_data)\n",
" cleaned_data = [x for x in mark_negation(tokenized_data) if x.replace(\"_NEG\", \"\") not in stop_words] # mark_negation(tokenized_data)\n",
" return label, cleaned_data\n",
"\n",
"sample_tuple = clean_data_with_negation(file_paths[0])\n",
"(sample_tuple[0], sample_tuple[1][5:10])"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('neg', ['church', 'party', 'drink', 'drive', 'get'])"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vHoSTC9cl9gX",
"colab_type": "code",
"outputId": "9a452116-c430-4c49-950c-85e32a6a27b1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"def clean_data(file_path):\n",
" '''\n",
" Helper function to read data from file and do basic preprocessing.\n",
" \n",
" inputs:\n",
" file_path : relative location of file.\n",
" \n",
" returns:\n",
" tuple consisting of:\n",
" label : whether emotion is positive (pos) or negative (neg)\n",
" cleaned_data : string after tokenization and taking out stop_words.\n",
" '''\n",
" label_pattern = re.compile('''\n",
" \\/(\\w{3})\\/.*\\.txt\n",
" ''', \n",
" re.IGNORECASE|re.VERBOSE)\n",
" review_pattern = re.compile('''\n",
" ([\\w]+)\n",
" ''', \n",
" re.IGNORECASE|re.VERBOSE)\n",
" \n",
" label = label_pattern.search(file_path).group(1)\n",
" \n",
" raw_data = get_data(file_path)\n",
" tokenized_data = review_pattern.findall(raw_data)\n",
" cleaned_data = tokenized_data\n",
" return label, cleaned_data\n",
"\n",
"sample_tuple = clean_data(file_paths[0])\n",
"(sample_tuple[0], sample_tuple[1][5:10])"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('neg', ['to', 'a', 'church', 'party', 'drink'])"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EidavHlxi2SV",
"colab_type": "code",
"outputId": "835abff5-124b-49d8-f249-4d97df01dd8c",
"colab": {
"resources": {
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definitions when they are used. */

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| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
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This file is copied from the JupyterLab project to define default styling for
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 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
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* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

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 */",
"ok": true,
"headers": [
[
"content-type",
"text/css"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 66
}
},
"source": [
"labels1 = list()\n",
"reviews1 = list()\n",
"reviews2 = list()\n",
"reviews3 = list()\n",
"\n",
"\n",
"for file in tqdm(file_paths): # A LOOOTtttt of files. Only uncomment when code bug free.\n",
"# for file in tqdm(small_paths):\n",
" label1, review1 = clean_data(file)\n",
" label2, review2 = clean_data_with_negation(file)\n",
" label3, review3 = clean_data_with_stopwords(file)\n",
" \n",
" labels1.append(label1)\n",
" reviews1.append(review1)\n",
" \n",
" reviews2.append(review2)\n",
" \n",
" reviews3.append(review3)\n",
" \n",
"movieDF = pd.DataFrame(np.column_stack([labels1, reviews1]), columns=['category', 'review'])\n",
"movieDF.loc[movieDF['category'] == 'pos', 'label'] = 1\n",
"movieDF.loc[movieDF['category'] == 'neg', 'label'] = -1\n",
"movieDF['category'] = movieDF['category'].astype(str)\n",
"movieDF['review'] = movieDF['review'].astype(object)\n",
"movieDF['label'] = movieDF['label'].astype(int)\n",
"\n",
"movieDF2 = pd.DataFrame(np.column_stack([labels1, reviews2]), columns=['category', 'review'])\n",
"movieDF2.loc[movieDF2['category'] == 'pos', 'label'] = 1\n",
"movieDF2.loc[movieDF2['category'] == 'neg', 'label'] = -1\n",
"movieDF2['category'] = movieDF2['category'].astype(str)\n",
"movieDF2['review'] = movieDF2['review'].astype(object)\n",
"movieDF2['label'] = movieDF2['label'].astype(int)\n",
"\n",
"movieDF3 = pd.DataFrame(np.column_stack([labels1, reviews3]), columns=['category', 'review'])\n",
"movieDF3.loc[movieDF3['category'] == 'pos', 'label'] = 1\n",
"movieDF3.loc[movieDF3['category'] == 'neg', 'label'] = -1\n",
"movieDF3['category'] = movieDF3['category'].astype(str)\n",
"movieDF3['review'] = movieDF3['review'].astype(object)\n",
"movieDF3['label'] = movieDF3['label'].astype(int)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6e124f40ed23432797bf36baccd9769c",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=2000), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "eXB64ij3nB1_",
"colab_type": "code",
"outputId": "ea49f718-181e-4502-a365-e6bfc9cf8a4d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"movieDF.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category</th>\n",
" <th>review</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>neg</td>\n",
" <td>[plot, two, teen, couples, go, to, a, church, ...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neg</td>\n",
" <td>[so, ask, yourself, what, 8mm, eight, millimet...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>neg</td>\n",
" <td>[synopsis, a, mentally, unstable, man, undergo...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>neg</td>\n",
" <td>[the, happy, bastard, s, quick, movie, review,...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>neg</td>\n",
" <td>[call, it, a, road, trip, for, the, walking, w...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category review label\n",
"0 neg [plot, two, teen, couples, go, to, a, church, ... -1\n",
"1 neg [so, ask, yourself, what, 8mm, eight, millimet... -1\n",
"2 neg [synopsis, a, mentally, unstable, man, undergo... -1\n",
"3 neg [the, happy, bastard, s, quick, movie, review,... -1\n",
"4 neg [call, it, a, road, trip, for, the, walking, w... -1"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ucZcmH5CEbSF",
"colab_type": "code",
"colab": {}
},
"source": [
"# labels = list()\n",
"# reviews = list()\n",
"\n",
"\n",
"# # for file in tqdm(file_paths): # A LOOOTtttt of files. Only uncomment when code bug free.\n",
"# for file in tqdm(small_paths):\n",
"# label, review = clean_data_with_negation(file)\n",
"# labels.append(label)\n",
"# reviews.append(review)\n",
" \n",
"# movieDF2 = pd.DataFrame(np.column_stack([labels, reviews]), columns=['category', 'review'])\n",
"# movieDF2.loc[movieDF2['category'] == 'pos', 'label'] = 1\n",
"# movieDF2.loc[movieDF2['category'] == 'neg', 'label'] = -1\n",
"# movieDF2['category'] = movieDF2['category'].astype(str)\n",
"# movieDF2['review'] = movieDF2['review'].astype(object)\n",
"# movieDF2['label'] = movieDF2['label'].astype(int)\n",
"# movieDF2['label'].hist(bins=2)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_AU9QUoYEbtA",
"colab_type": "code",
"outputId": "9aff1d87-a944-4a18-dfe8-416e6db0910b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"movieDF2.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category</th>\n",
" <th>review</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>neg</td>\n",
" <td>[plot, two, teen, couples, go, church, party, ...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neg</td>\n",
" <td>[ask, 8mm, eight, millimeter, really, wholesom...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>neg</td>\n",
" <td>[synopsis, mentally, unstable, man, undergoing...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>neg</td>\n",
" <td>[happy, bastard, quick, movie, review, damn, y...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>neg</td>\n",
" <td>[call, road, trip, walking, wounded, stellan, ...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category review label\n",
"0 neg [plot, two, teen, couples, go, church, party, ... -1\n",
"1 neg [ask, 8mm, eight, millimeter, really, wholesom... -1\n",
"2 neg [synopsis, mentally, unstable, man, undergoing... -1\n",
"3 neg [happy, bastard, quick, movie, review, damn, y... -1\n",
"4 neg [call, road, trip, walking, wounded, stellan, ... -1"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "cbRPS9uPRxLH",
"colab": {}
},
"source": [
"# labels = list()\n",
"# reviews = list()\n",
"\n",
"\n",
"# # for file in tqdm(file_paths): # A LOOOTtttt of files. Only uncomment when code bug free.\n",
"# for file in tqdm(small_paths):\n",
"# label, review = clean_data_with_negation(file)\n",
"# labels.append(label)\n",
"# reviews.append(review)\n",
" \n",
"# movieDF3 = pd.DataFrame(np.column_stack([labels, reviews]), columns=['category', 'review'])\n",
"# movieDF3.loc[movieDF3['category'] == 'pos', 'label'] = 1\n",
"# movieDF3.loc[movieDF3['category'] == 'neg', 'label'] = -1\n",
"# movieDF3['category'] = movieDF3['category'].astype(str)\n",
"# movieDF3['review'] = movieDF3['review'].astype(object)\n",
"# movieDF3['label'] = movieDF3['label'].astype(int)\n",
"# movieDF3['label'].hist(bins=2)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Eo7pHLvcRxLK",
"outputId": "02d262b5-0bc4-445c-cbcb-d96af478be98",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"movieDF3.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category</th>\n",
" <th>review</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>neg</td>\n",
" <td>[plot, two, teen, couples, go, church, party, ...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neg</td>\n",
" <td>[ask, 8mm, eight, millimeter, really, wholesom...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>neg</td>\n",
" <td>[synopsis, mentally, unstable, man, undergoing...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>neg</td>\n",
" <td>[happy, bastard, quick, movie, review, damn, y...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>neg</td>\n",
" <td>[call, road, trip, walking, wounded, stellan, ...</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category review label\n",
"0 neg [plot, two, teen, couples, go, church, party, ... -1\n",
"1 neg [ask, 8mm, eight, millimeter, really, wholesom... -1\n",
"2 neg [synopsis, mentally, unstable, man, undergoing... -1\n",
"3 neg [happy, bastard, quick, movie, review, damn, y... -1\n",
"4 neg [call, road, trip, walking, wounded, stellan, ... -1"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yY16lj6ToZgE",
"colab_type": "text"
},
"source": [
"### Import EmoLex"
]
},
{
"cell_type": "code",
"metadata": {
"id": "h2EhdgYjm4TU",
"colab_type": "code",
"outputId": "8810951b-bf2f-4468-a47f-f3870dc7bd63",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"emoDF = pd.read_csv(emolex_path, sep=\"\\t\", header=None, names=['word', 'sense', 'is_sense'])\n",
"emoDF = pd.pivot_table(data=emoDF, values='is_sense', index='word', columns='sense').fillna(0).reset_index()\n",
"\n",
"mask = ~(emoDF['negative'].astype(bool) | emoDF['positive'].astype(bool))\n",
"emoDF.loc[mask, 'neutral'] = 1\n",
"emoDF.loc[~mask, 'neutral'] = 0\n",
"emoDF['neutral'] = emoDF['neutral'].astype(int)\n",
"emoDF = emoDF[['word', 'negative', 'positive', 'neutral']]\n",
"emoDF.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>sense</th>\n",
" <th>word</th>\n",
" <th>negative</th>\n",
" <th>positive</th>\n",
" <th>neutral</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>aback</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>abacus</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>abandon</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>abandoned</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>abandonment</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"sense word negative positive neutral\n",
"0 aback 0 0 1\n",
"1 abacus 0 0 1\n",
"2 abandon 1 0 0\n",
"3 abandoned 1 0 0\n",
"4 abandonment 1 0 0"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ywsc921tPf6y",
"colab_type": "text"
},
"source": [
"#### Add Negative entries to EmoLex"
]
},
{
"cell_type": "code",
"metadata": {
"id": "yvxz8pCHPfLI",
"colab_type": "code",
"outputId": "5330c651-5eed-471c-c580-a4451189d83c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"emoDF2 = emoDF.copy(deep=True)\n",
"\n",
"emoDF2['word'] = emoDF2['word'] + \"_NEG\"\n",
"emoDF2['negative'] = ~(emoDF2['negative'].astype(bool) | emoDF2['neutral'].astype(bool))\n",
"emoDF2['positive'] = ~(emoDF2['positive'].astype(bool) | emoDF2['neutral'].astype(bool))\n",
"emoDF2['negative'] = emoDF2['negative'].astype(int)\n",
"emoDF2['positive'] = emoDF2['positive'].astype(int)\n",
"\n",
"neg_emoDF = pd.concat([emoDF2, emoDF])\n",
"neg_emoDF.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>sense</th>\n",
" <th>word</th>\n",
" <th>negative</th>\n",
" <th>positive</th>\n",
" <th>neutral</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>aback_NEG</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>abacus_NEG</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>abandon_NEG</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>abandoned_NEG</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>abandonment_NEG</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"sense word negative positive neutral\n",
"0 aback_NEG 0 0 1\n",
"1 abacus_NEG 0 0 1\n",
"2 abandon_NEG 0 1 0\n",
"3 abandoned_NEG 0 1 0\n",
"4 abandonment_NEG 0 1 0"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HWvgjRRk7xuQ",
"colab_type": "text"
},
"source": [
"### Import SentiWordNet"
]
},
{
"cell_type": "code",
"metadata": {
"id": "z0i0-9s671ZA",
"colab_type": "code",
"outputId": "0a674896-6f07-47d4-e4d6-bb3fb2816a3a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"sen_wordDF = pd.read_csv(\"/gdrive/My Drive/SPRING_2019/LING406/final_project/SentiWordNet/SentiWordNet_3.0.0.txt\", sep='\\t').reset_index(drop=True)\n",
"sen_wordDF = (pd.DataFrame(sen_wordDF['SynsetTerms']\n",
" .str.split(\" \")\n",
" .tolist())\n",
" .merge(sen_wordDF, left_index = True, right_index = True)\n",
" .drop('SynsetTerms', axis=1)\n",
" .melt(id_vars = ['POS ID', 'PosScore', 'NegScore', 'Gloss'], value_name = \"SynsetTerm\")\n",
" .dropna()\n",
")\n",
"sen_wordDF['Score'] = sen_wordDF['PosScore'] - sen_wordDF['NegScore']\n",
"sen_wordDF['SynsetTerm'] = sen_wordDF['SynsetTerm'].apply(lambda x : x.split(\"#\")[0].replace(\"_\", \" \"))\n",
"sen_wordDF = sen_wordDF[['SynsetTerm', 'Score']]\n",
"sen_wordDF.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SynsetTerm</th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>able</td>\n",
" <td>0.125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>unable</td>\n",
" <td>-0.750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>dorsal</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ventral</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>acroscopic</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SynsetTerm Score\n",
"0 able 0.125\n",
"1 unable -0.750\n",
"2 dorsal 0.000\n",
"3 ventral 0.000\n",
"4 acroscopic 0.000"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "liLAfHpbZJjW"
},
"source": [
"#### Add Negative entries to SentiWordNet"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "dMnTzwPkZJja",
"outputId": "817cc782-7254-4853-9473-cc317c2def7c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"sen_wordDF2 = sen_wordDF.copy(deep=True)\n",
"\n",
"sen_wordDF2['SynsetTerm'] = sen_wordDF2['SynsetTerm'] + \"_NEG\"\n",
"sen_wordDF2['Score'] = -1 * sen_wordDF2['Score']\n",
"\n",
"neg_sen_wordDF = pd.concat([sen_wordDF2, sen_wordDF])\n",
"neg_sen_wordDF.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SynsetTerm</th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>able_NEG</td>\n",
" <td>-0.125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>unable_NEG</td>\n",
" <td>0.750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>dorsal_NEG</td>\n",
" <td>-0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ventral_NEG</td>\n",
" <td>-0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>acroscopic_NEG</td>\n",
" <td>-0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SynsetTerm Score\n",
"0 able_NEG -0.125\n",
"1 unable_NEG 0.750\n",
"2 dorsal_NEG -0.000\n",
"3 ventral_NEG -0.000\n",
"4 acroscopic_NEG -0.000"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mioYbZKkGZZT",
"colab_type": "text"
},
"source": [
"## Experiment 1: Naive Approach\n",
"\n",
"**Hypothesis**: The sentiment of a sentence can be determined by the number of times the word occurs with that rating **in the dataset**.\n",
"\n",
"**Approach**:\n",
"Basic approach is as follows:\n",
"1. Normalize Data.\n",
"1. Filter Data.\n",
"1. count the number of times a word occurs with that rating.\n",
"1. take the mean of this measure across all ratings for each word.\n",
"1. Put this in a dict.\n",
"1. For each sentence, compute the 'average rating' by looking up the average rate for each word from the dict, summing it up and dividing by length of sentence.\n",
"\n",
"**Observations**:\n",
"1. Precision = **0.691**\n",
"1. Recall = **0.691**\n",
"1. F_Measure = **0.69**\n",
"\n",
"**Conclusion**: This sucks."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gLdOmNkUEKrW",
"colab_type": "text"
},
"source": [
"### Normalize Expt. 1\n",
"Number of data points in each class are unbalanced. We \"cutoff\" the data points in each class once they exceed the min data points."
]
},
{
"cell_type": "code",
"metadata": {
"id": "EPlcNj41-lR6",
"colab_type": "code",
"outputId": "360e3db8-07aa-4cb4-a7b5-f3260be3e5ed",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
}
},
"source": [
"def cutoff_excess(movieDF):\n",
" min_class = int(movieDF.groupby('label')['category'].count().min())\n",
" naive_normDF = movieDF.groupby('label').head(min_class)\n",
" cutoffDF = movieDF[~movieDF.isin(naive_normDF)].dropna()\n",
" return naive_normDF, cutoffDF\n",
"\n",
"naive_normDF, cutoffDF = cutoff_excess(movieDF)\n",
"naive_normDF['label'].hist(), cutoffDF['label'].hist(bins=2)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(<matplotlib.axes._subplots.AxesSubplot at 0x7f10ee5549e8>,\n",
" <matplotlib.axes._subplots.AxesSubplot at 0x7f10ee5549e8>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
},
{
"output_type": "display_data",
"data": {
"image/png": 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HzEpdM2BdrbGu1lhXC1Ze0VFdb5zJoH4HwCiwtGl9SWn7hczcCGzsxsEi4p7MHOzGvrrJ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\nVer/ASK/tGh8l0uGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EyV18luAJWru",
"colab_type": "text"
},
"source": [
"### Count occurances\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "dm5H_qST2B4c",
"colab_type": "code",
"outputId": "706c9250-5f43-464c-c5ea-f71f4068d4ee",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"def get_word_counts(normDF):\n",
" countDF = (normDF['review']\n",
" .apply(pd.Series)\n",
" .merge(normDF, left_index = True, right_index = True)\n",
" .drop('review', axis=1)\n",
" .melt(id_vars = ['label', 'category'], value_name = \"review_word\")\n",
" )\n",
" return countDF\n",
"countDF = get_word_counts(naive_normDF)\n",
"countDF.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>category</th>\n",
" <th>variable</th>\n",
" <th>review_word</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>plot</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>so</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>synopsis</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>the</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>call</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label category variable review_word\n",
"0 -1 neg 0 plot\n",
"1 -1 neg 0 so\n",
"2 -1 neg 0 synopsis\n",
"3 -1 neg 0 the\n",
"4 -1 neg 0 call"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wP4cFEV1yGqX",
"colab_type": "text"
},
"source": [
"### Generate dict"
]
},
{
"cell_type": "code",
"metadata": {
"id": "18si7SXs-_Z3",
"colab_type": "code",
"outputId": "c2be6580-2685-4717-aa3a-ed0e3793afc9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"def get_dict_from_counts(countDF):\n",
" test = (countDF.groupby(['label', 'review_word']).count().reset_index())\n",
" test['tot'] = test['label'] * test['variable']\n",
" test = test.groupby(['review_word'])['tot'].mean().sort_values(ascending=False).reset_index()\n",
" sen_words = defaultdict(float, dict(zip(test['review_word'], test['tot'])))\n",
" return sen_words\n",
"\n",
"sentiment_dict = get_dict_from_counts(countDF)\n",
"\n",
"sentiment_dict['bad'], sentiment_dict['worst'], sentiment_dict['okay'], sentiment_dict['good'], sentiment_dict['amazing']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(-336.5, -105.0, -6.5, 42.5, 25.0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ijcn3IZGyQHA",
"colab_type": "text"
},
"source": [
"### Enrich data using dict"
]
},
{
"cell_type": "code",
"metadata": {
"id": "HJw0wvOmAB9l",
"colab_type": "code",
"outputId": "921b509f-f6db-4d50-ab16-76bc3046eb1c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"def lookup_sentiment(testDF, test_dict):\n",
" outDF = testDF.copy(deep=True)\n",
" outDF['sentiment']= testDF['review'].apply(lambda x: (sum([test_dict[y] for y in x])) / len(x))\n",
" return outDF\n",
"\n",
"testDF = lookup_sentiment(movieDF, sentiment_dict)\n",
"testDF.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category</th>\n",
" <th>review</th>\n",
" <th>label</th>\n",
" <th>sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>neg</td>\n",
" <td>[plot, two, teen, couples, go, to, a, church, ...</td>\n",
" <td>-1</td>\n",
" <td>360.422176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neg</td>\n",
" <td>[so, ask, yourself, what, 8mm, eight, millimet...</td>\n",
" <td>-1</td>\n",
" <td>358.972469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>neg</td>\n",
" <td>[synopsis, a, mentally, unstable, man, undergo...</td>\n",
" <td>-1</td>\n",
" <td>425.452540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>neg</td>\n",
" <td>[the, happy, bastard, s, quick, movie, review,...</td>\n",
" <td>-1</td>\n",
" <td>387.820158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>neg</td>\n",
" <td>[call, it, a, road, trip, for, the, walking, w...</td>\n",
" <td>-1</td>\n",
" <td>336.355082</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category review label \\\n",
"0 neg [plot, two, teen, couples, go, to, a, church, ... -1 \n",
"1 neg [so, ask, yourself, what, 8mm, eight, millimet... -1 \n",
"2 neg [synopsis, a, mentally, unstable, man, undergo... -1 \n",
"3 neg [the, happy, bastard, s, quick, movie, review,... -1 \n",
"4 neg [call, it, a, road, trip, for, the, walking, w... -1 \n",
"\n",
" sentiment \n",
"0 360.422176 \n",
"1 358.972469 \n",
"2 425.452540 \n",
"3 387.820158 \n",
"4 336.355082 "
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xM_y_uIZCl-s",
"colab_type": "text"
},
"source": [
"### Testing on entire DF"
]
},
{
"cell_type": "code",
"metadata": {
"id": "clzs46kxvEQ8",
"colab_type": "code",
"colab": {}
},
"source": [
"def get_prf_from_df(trainDF, pred_name='prediction', label_name='labels'):\n",
" label_dict = defaultdict(set)\n",
" predict_dict = defaultdict(set)\n",
"\n",
" for prediction in trainDF[pred_name].unique():\n",
" predict_dict[prediction] = set(trainDF.index[trainDF[pred_name] == prediction].tolist())\n",
"\n",
" for label in trainDF[label_name].unique():\n",
" label_dict[label] = set(trainDF.index[trainDF[label_name] == label].tolist())\n",
"\n",
" prec = float()\n",
" rec = float()\n",
" f_meas = float()\n",
" for key in label_dict:\n",
" p = precision(label_dict[key], predict_dict[key])\n",
" r = recall(label_dict[key], predict_dict[key])\n",
" f = f_measure(label_dict[key], predict_dict[key])\n",
" if p is not None: prec += p / len(label_dict.keys())\n",
" if r is not None: rec += r / len(label_dict.keys())\n",
" if f is not None: f_meas += f / len(label_dict.keys())\n",
"\n",
" return (prec, rec, f_meas)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JkdACaO3CyuQ",
"colab_type": "code",
"outputId": "c117ef20-1c4a-48c2-bd78-872254d7df4d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
}
},
"source": [
"def test_df(testDF, is_print=False):\n",
" measuresDF = testDF.groupby('label')['sentiment'].mean().reset_index()\n",
" mean_rates = np.asarray(measuresDF['sentiment'].tolist())\n",
" testDF['predicted_label'] = testDF['sentiment'].apply(lambda x : -1 if x <= mean_rates.mean() else 1)\n",
" if is_print:\n",
" print(('precision', 'recall', 'f_measure'), ' = ', get_prf_from_df(testDF, 'label', 'predicted_label'))\n",
" return get_prf_from_df(testDF, 'label', 'predicted_label'), testDF.groupby('label')['predicted_label'].mean().reset_index()\n",
"\n",
"test_df(testDF)[1]"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>predicted_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1</td>\n",
" <td>-0.258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0.236</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label predicted_label\n",
"0 -1 -0.258\n",
"1 1 0.236"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8_2HbJNCyY3N",
"colab_type": "text"
},
"source": [
"### Calculating metrics"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iorhlNuLwAKx",
"colab_type": "code",
"outputId": "b140eac6-15ef-40a8-909c-4ea0c418e239",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"def test_n_fold_1(testingDF, num_folds = 10):\n",
" featureDF = testingDF.sample(frac=1)\n",
"\n",
" cum_model_precision = float()\n",
" cum_model_recall = float()\n",
" cum_model_f = float()\n",
"\n",
" k_fold_gen = KFold(n_splits=num_folds)\n",
" for train_idx, test_idx in k_fold_gen.split(featureDF):\n",
"\n",
" train = featureDF.iloc[train_idx]\n",
" test = featureDF.iloc[test_idx]\n",
" words_dict = get_dict_from_counts(get_word_counts(cutoff_excess(train)[0]))\n",
" enrichedDF = lookup_sentiment(test, words_dict)\n",
" model_precision, model_recall, model_f = test_df(enrichedDF)[0]\n",
" cum_model_precision += model_precision\n",
" cum_model_recall += model_recall\n",
" cum_model_f += model_f\n",
"\n",
" cum_model_precision = round(cum_model_precision / num_folds, 3)\n",
" cum_model_recall = round(cum_model_recall / num_folds, 3)\n",
" cum_model_f = round(cum_model_f / num_folds, 3)\n",
"\n",
" print(('final precision', 'final recall', 'final f_measure'), ' = ', (cum_model_precision, cum_model_recall, cum_model_f))\n",
"\n",
"for key, value in (zip([\"vanilla\", \"with negation & no stopwords\",\"no stopwords\"], [movieDF, movieDF2, movieDF3])):\n",
" print(key)\n",
"# print(\"vanilla\")\n",
" test_n_fold_1(value, num_folds = 10)\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"vanilla\n",
"('final precision', 'final recall', 'final f_measure') = (0.615, 0.615, 0.614)\n",
"with negation & no stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.682, 0.682, 0.681)\n",
"no stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.691, 0.691, 0.69)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sbU5XrdLycpl",
"colab_type": "text"
},
"source": [
"### Explaining performance\n",
"\n",
"Most words fall under the neutral category. Hence dont effect perf. Additionally, the dict has incorrect values for a lot of words. Also there is a lot of noise in the dict. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "jxLA5hJRE4Jc",
"colab_type": "code",
"outputId": "704704d7-528b-4483-d55d-cfa1625cc898",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173
}
},
"source": [
"testDF.groupby('label').head(2).sort_values('label')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category</th>\n",
" <th>review</th>\n",
" <th>label</th>\n",
" <th>sentiment</th>\n",
" <th>predicted_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>neg</td>\n",
" <td>[plot, two, teen, couples, go, to, a, church, ...</td>\n",
" <td>-1</td>\n",
" <td>360.422176</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neg</td>\n",
" <td>[so, ask, yourself, what, 8mm, eight, millimet...</td>\n",
" <td>-1</td>\n",
" <td>358.972469</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>pos</td>\n",
" <td>[films, adapted, from, comic, books, have, had...</td>\n",
" <td>1</td>\n",
" <td>393.895105</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1001</th>\n",
" <td>pos</td>\n",
" <td>[every, now, and, then, a, movie, comes, along...</td>\n",
" <td>1</td>\n",
" <td>411.541667</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category review label \\\n",
"0 neg [plot, two, teen, couples, go, to, a, church, ... -1 \n",
"1 neg [so, ask, yourself, what, 8mm, eight, millimet... -1 \n",
"1000 pos [films, adapted, from, comic, books, have, had... 1 \n",
"1001 pos [every, now, and, then, a, movie, comes, along... 1 \n",
"\n",
" sentiment predicted_label \n",
"0 360.422176 -1 \n",
"1 358.972469 -1 \n",
"1000 393.895105 -1 \n",
"1001 411.541667 1 "
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "AmnsqRN6-0nV",
"colab_type": "code",
"outputId": "3e456a6c-055d-4e66-f067-b2989e5582b4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# bad learnings >:(\n",
"sentiment_dict['disgust'], sentiment_dict['shit'], sentiment_dict['okay'], sentiment_dict['like']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1.5, 2.0, -6.5, -43.0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "YlIWI2PN_eDh",
"outputId": "c60b1576-11c2-4839-b1cc-64e6e613b9e9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 733
}
},
"source": [
"get_random = lambda obj: obj.loc[np.random.choice(obj.index, 1, True),:]\n",
"reviews = testDF.groupby('label').apply(get_random)['review'].tolist()\n",
"# reviews = featureDF.groupby('label').head(1).sort_values('label')['review'].tolist()\n",
"\n",
"fig, ax = plt.subplots(len(reviews),1, figsize=(10,10), constrained_layout=True)\n",
"\n",
"props = dict(boxstyle='square', facecolor='white', alpha=0.5)\n",
"\n",
"for i in range(0, len(reviews)):\n",
" review = reviews[i]\n",
" ax[i].hist([sentiment_dict[word] for word in review], bins=50)\n",
" ax[i].set_title(str(\"label = \" + str(2*i - 1)) + \", len = \" + str(len(review)))\n",
"\n",
"\n",
"\n",
"fig.suptitle('Histogram of sentiment lookup for each word in ' + str(len(reviews)) + ' classes', fontsize=16)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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kScvNbEawjwL2GSs7HrhPVe0GnAW8ZGTZ96tq9+FySJ9mSpIkSSvDjAG7qr4I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\nQP8DZ1p/gD3OfDQAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s3CxnLCyylwV",
"colab_type": "text"
},
"source": [
"## Experiment 2: Using Emolex to enrich data\n",
"\n",
"**Hypothesis**: The sentiment of a sentence can be determined by the number of times the word occurs with that rating **in the EmoLex dataset**.\n",
"\n",
"**Approach**:\n",
"Basic approach is as follows:\n",
"1. Convert emotex to a dict with positive, negative and neutral labels.\n",
"1. For each sentence, compute the 'average rating' by looking up the average rate from the dict.\n",
"\n",
"**Observations**:\n",
"1. Precision = **0.651**\n",
"1. Recall = **0.651**\n",
"1. F_Measure = **0.65**\n",
"\n",
"**Conclusion**: Same problem as before. most words are classified into the 'neutral' category. We lose info from that."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qeJJtzzLzGxl",
"colab_type": "text"
},
"source": [
"### Converting EmoTex"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XXwqWMYxykp9",
"colab_type": "code",
"colab": {}
},
"source": [
"emoDF\n",
"emo_dict = pd.melt(emoDF, id_vars=['word'], value_vars=['negative', 'positive', 'neutral']).query('value != 0').drop('value', axis=1)\n",
"\n",
"emo_dict.loc[emo_dict['sense'] == 'negative', 'label'] = -1\n",
"emo_dict.loc[emo_dict['sense'] == 'positive', 'label'] = 1\n",
"emo_dict.loc[emo_dict['sense'] == 'neutral', 'label'] = 0\n",
"\n",
"emo_dict = defaultdict(float, dict(zip(emo_dict['word'], emo_dict['label'])))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "BIgY0k484DUE",
"colab_type": "text"
},
"source": [
"### Calculating metrics"
]
},
{
"cell_type": "code",
"metadata": {
"id": "TPnrq0vKYza0",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hGRowywJ3gBl",
"colab_type": "code",
"outputId": "a2f59e48-b36f-4b34-fe3f-42c84c2e074f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"def test_n_fold_2(testingDF, num_folds = 10):\n",
" cum_model_precision = float()\n",
" cum_model_recall = float()\n",
" cum_model_f = float()\n",
"\n",
" featureDF = testingDF.sample(frac=1)\n",
"\n",
" results = test_df(lookup_sentiment(featureDF, emo_dict))\n",
"\n",
" cum_model_precision = round(results[0][0], 3)\n",
" cum_model_recall = round(results[0][1], 3)\n",
" cum_model_f = round(results[0][2], 3)\n",
" print(('final precision', 'final recall', 'final f_measure'), ' = ', (cum_model_precision, cum_model_recall, cum_model_f))\n",
"\n",
"\n",
"\n",
"for key, value in (zip([\"vanilla\", \"with negation & no stopwords\",\"no stopwords\"], [movieDF, movieDF2, movieDF3])):\n",
" print(key)\n",
"# print(\"vanilla\")\n",
" test_n_fold_2(value)\n",
" \n",
"featureDF = movieDF.sample(frac=1)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"vanilla\n",
"('final precision', 'final recall', 'final f_measure') = (0.647, 0.648, 0.647)\n",
"with negation & no stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.56, 0.564, 0.552)\n",
"no stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.651, 0.651, 0.65)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X89vq1N2542-",
"colab_type": "text"
},
"source": [
"### Explaining results\n",
"We have taken care of the problem of having bad data for a lot of dict entries but we are still losing a lot of info as most words don't contribute to calculating the sentiment."
]
},
{
"cell_type": "code",
"metadata": {
"id": "rPi5FiAb_5Of",
"colab_type": "code",
"outputId": "f3a5d13a-530a-4936-ff4d-4db40fd95c42",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# no more bad learnings :)\n",
"emo_dict['disgust'], emo_dict['shit'], emo_dict['okay'], emo_dict['like']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(-1.0, -1.0, 0.0, 0.0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "S-A-GzP-5rR9",
"colab_type": "code",
"outputId": "eedd13db-1f93-4589-84f3-3201966302ac",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 733
}
},
"source": [
"get_random = lambda obj: obj.loc[np.random.choice(obj.index, 1, True),:]\n",
"reviews = featureDF.groupby('label').apply(get_random)['review'].tolist()\n",
"# reviews = featureDF.groupby('label').head(1).sort_values('label')['review'].tolist()\n",
"\n",
"fig, ax = plt.subplots(len(reviews),1, figsize=(10,10), constrained_layout=True)\n",
"\n",
"for i in range(0, len(reviews)):\n",
" review = reviews[i]\n",
" ax[i].hist([emo_dict[word] for word in review], bins=50)\n",
" ax[i].set_title(str(\"label = \" + str(2*i - 1)) + \", len = \" + str(len(review)))\n",
"\n",
"fig.suptitle('Sentiment of each word in ' + str(len(reviews)) + ' sample labels', fontsize=16)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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BGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEb\nAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAA\nBhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYS\nsAEAYCABGwAABhKwAQBgoDUDdlW9qqouqKpPLJRdv6pOrKrPzs/7zeVVVS+sqjOr6vSqusPubDwA\nAGw06zmDfVySey8rOzbJSd19cJKT5vEkuU+Sg+fHMUleMqaZAABw5bBmwO7u9yW5cFnx4UmOn4eP\nT3LEQvmre3JKkn2r6kajGgsAABvdzvbBvmF3nzcPn5/khvPwAUnOXqh3zlx2OVV1TFWdWlWnbtu2\nbSebAQAAG8su/8ixuztJ78R8L+vuQ7v70C1btuxqMwAAYEPY2YD95aWuH/PzBXP5uUkOWqh34FwG\nAACbws4G7BOSHDUPH5XkrQvlD5+vJnKnJBctdCUBAIC93j5rVaiq1ya5W5L9q+qcJM9I8twkb6iq\no5N8McmD5+rvSHJYkjOTfDvJI3dDmwEAYMNaM2B390NWmXTPFep2ksftaqMAAODKyp0cAQBgIAEb\nAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAA\nBhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYS\nsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErAB\nAGAgARsAAAYSsAEAYKB99nQDAADYnLYe+/Ydnues5953N7RkLGewAQBgIAEbAAAGErABAGCgTd8H\ne2/t+wMAwJ7hDDYAAAwkYAMAwEACNgAADCRgAwDAQAI2AAAMJGADAMBAAjYAAAwkYAMAwEACNgAA\nDCRgAwDAQAI2AAAMJGADAMBAAjYAAAwkYAMAwEC7LWBX1b2r6tNVdWZVHbu71gMAABvJbgnYVXWV\nJC9Ocp8kt07ykKq69e5YFwAAbCS76wz2HZOc2d2f7+7vJXldksN307oAAGDD2Gc3LfeAJGcvjJ+T\n5D8vVqiqY5IcM49eXFWf3k1tWcv+Sb6yIzPU83ZTS65cdni/kcR+21n2247z3rZzHGs7x37bOfbb\nTqjn7dH9dtP1VNpdAXtN3f2yJC/bU+tfUlWndvehe7odVzb2286x33aO/bbj7LOdY7/tHPtt59hv\nO+fKsN92VxeRc5MctDB+4Fw+kd1vAAAgAElEQVQGAAB7td0VsD+c5OCqullVXS3JkUlO2E3rAgCA\nDWO3dBHp7kur6vFJ3pnkKkle1d2f3B3rGmCPd1O5krLfdo79tnPstx1nn+0c+23n2G87x37bORt+\nv1V37+k2AADAXsOdHAEAYCABGwAABtoUAbuqHlRVn6yqH1TVqpd1We327vOPNT80l79+/uHmXq+q\nrl9VJ1bVZ+fn/Vaoc/eqOm3h8d2qOmKedlxVfWFh2iFX/FZc8daz3+Z6ly3smxMWyjfd8bbOY+2Q\nqvrg/Fo+var+68K0TXWsrfZetTD96vOxc+Z8LG1dmPbUufzTVfUrV2S797R17Lf/UVWfmo+vk6rq\npgvTVny9bgbr2G+PqKptC/vnvy1MO2p+XX+2qo66Ylu+56xjn71gYX99pqq+vjBtMx9rr6qqC6rq\nE6tMr6p64bxfT6+qOyxM21jHWnfv9Y8kP5nkJ5K8J8mhq9S5SpLPJbl5kqsl+ViSW8/T3pDkyHn4\npUl+c09v0xW03/44ybHz8LFJnrdG/esnuTDJj87jxyV54J7ejo2635JcvEr5pjve1rPPktwyycHz\n8I2TnJdk33l80xxr23uvWqjz2CQvnYePTPL6efjWc/2rJ7nZvJyr7Olt2kD77e4L71+/ubTf5vEV\nX697+2Od++0RSV60wrzXT/L5+Xm/eXi/Pb1NG2GfLav/3zNdDGJTH2vztv9ikjsk+cQq0w9L8vdJ\nKsmdknxoLt9wx9qmOIPd3Wd091p3ilzx9u5VVUnukeSNc73jkxyx+1q7oRyeaXuT9W33A5P8fXd/\ne7e2auPb0f327zbx8bbmPuvuz3T3Z+fhLyW5IMmWK6yFG8eK71XL6izuzzcmued8bB2e5HXdfUl3\nfyHJmfPyNoM191t3n7zw/nVKpns4bHbrOd5W8ytJTuzuC7v7a0lOTHLv3dTOjWRH99lDkrz2CmnZ\nBtfd78t0om41hyd5dU9OSbJvVd0oG/BY2xQBe51Wur37AUlukOTr3X3psvLN4Ibdfd48fH6SG65R\n/8hc/k3i2fPXOC+oqqsPb+HGtN79do2qOrWqTlnqVpPNe7zt0LFWVXfMdGbocwvFm+VYW+29asU6\n87F0UaZjaz3z7q12dNuPznSmbMlKr9fNYL377f+bX39vrKqlG81t1uNt3ds9d0O6WZJ3LxRv1mNt\nPVbbtxvuWNtjt0ofrareleTHVpj0e9391iu6PVcW29tviyPd3VW16jUd50+QP53p2udLnpopLF0t\n0zUrfzfJH+5qmzeCQfvtpt19blXdPMm7q+rjmYLQXmnwsfbXSY7q7h/MxXvtscYVr6p+PcmhSe66\nUHy512t3f27lJWw6f5fktd19SVU9OtO3J/fYw226sjgyyRu7+7KFMsfaXmCvCdjd/Uu7uIjVbu/+\n1UxfQewznwnaq277vr39VlVfrqobdfd5c6i5YDuLenCSt3T39xeWvXRG8pKq+qskTx7S6A1gxH7r\n7nPn589X1XuS3D7Jm7KXHm8j9llVXTfJ2zN9cD5lYdl77bG2gtXeq1aqc05V7ZPkepney9Yz795q\nXdteVb+U6UPfXbv7kqXyVV6vmyH0rLnfuvurC6OvyPSbiqV577Zs3vcMb+HGsyOvsyOTPG6xYBMf\na+ux2r7dcMeaLiI/tOLt3XvqPX9ypv7FSXJUks1yRvyETNubrL3dl+tDNgelpX7FRyRZ8VfBe6E1\n91tV7bfUjaGq9k9ylySf2sTH23r22dWSvCVT/7s3Lpu2mY61Fd+rltVZ3J8PTPLu+dg6IcmRNV1l\n5GZJDk7yz1dQu/e0NfdbVd0+yV8muX93X7BQvuLr9Qpr+Z61nv12o4XR+yc5Yx5+Z5J7zftvvyT3\nyn/8lnNvtZ7XaKrqVpl+kPfBhbLNfKytxwlJHj5fTeROSS6aT7BsvGNtT/7C8op6JHlApv44lyT5\ncpJ3zuU3TvKOhXqHJflMpk+Kv7dQfvNM/4TOTPJ/k1x9T2/TFbTfbpDkpCSfTfKuJNefyw9N8oqF\nelszfXr8kWXzvzvJxzOFnb9Jcu09vU0bZb8l+bl533xsfj56Mx9v69xnv57k+0lOW3gcshmPtZXe\nqzJ1ibn/PHyN+dg5cz6Wbr4w7+/N8306yX329LZssP32rvl/xNLxdcJcvurrdTM81rHfnpPkk/P+\nOTnJrRbmfdR8HJ6Z5JF7els2yj6bx5+Z5LnL5tvsx9prM10h6vuZctvRSR6T5DHz9Ery4nm/fjwL\nV4bbaMeaW6UDAMBAuogAAMBAAjYAAAwkYAMAwEACNgAADCRgAwDAQAI2AAAMJGADAMBAAjYAAAwk\nYAMAwEACNgAADCRgAwDAQAI2AAAMJGADAMBAAjYAAAwkYAObRlWdVVW/tM66XVW32Mn17PS8u0NV\nHVdVz9rT7QDYLARsgCuBqnp8VZ1aVZdU1XF7uj27oqqOrKpPV9VFVXVBVR1fVdddmH79qnpLVX2r\nqr5YVb+2MO1uVfWDqrp44XHUntkSgJUJ2ABXDl9K8qwkr9rTDRng/yW5S3dfL8nNk+yTaduWvDjJ\n95LcMMlDk7ykqm6zMP1L3X3thcfxV1TDAdZDwAY2paq6Y1V9sKq+XlXnVdWLqupqy6odVlWfr6qv\nVNXzq+pHFuZ/VFWdUVVfq6p3VtVNd2d7u/vN3f23Sb66q8uqqvtV1Wnztn+gqm67MO2sqnpyVZ0+\nn2F+fVVdY1fXuai7z+7urywUXZbkFvP6r5Xk/0vy+919cXe/P8kJSR42sg0Au5OADWxWlyX5rST7\nJ7lzknsmeeyyOg9IcmiSOyQ5PMmjkqSqDk/ytCS/mmRLkn9K8tr1rLSq/mIOtis9Th+wXWut//aZ\nzoI/OskNkvxlkhOq6uoL1R6c5N5Jbpbktkkescqyfn472/L1qvr57bTj56vqoiTfzBSo/2yedMsk\nl3b3ZxaqfyzJ4hns/1RVX66qL1TVC+ZQDrBhCNjAptTdH+nuU7r70u4+K1PQvOuyas/r7gu7+98y\nBcCHzOWPSfKc7j6juy9N8kdJDlnPWezufmx377vK47ZrzT/AMUn+srs/1N2Xzd0rLklyp4U6L+zu\nL3X3hUn+LskhKy2ou9+/nW3Zdz77vKJ53uslOTDJ85OcNU+6dpJvLKt+UZLrzMP/OrfnRknukeRn\nkvzp+jcfYPcTsIFNqapuWVVvq6rzq+obmULy/suqnb0w/MUkN56Hb5rkz5fO1Ca5MEklOWB3t3uA\nmyb57cUzzUkOyg+3LUnOXxj+dqbQu1t097lJ/iHJ6+aii5Ncd1m162Y6053uPr+7P9XdP+juLyT5\nnUxnwAE2DAEb2Kxekuls6MHdfd1MXT5qWZ2DFoZvkumHhskUvB+97GztNbv7A2uttKpeuuwKGIuP\nTw7YrrWcneTZy9r+o929ri4ui6rqF7azLRdX1S+sc1H7JPnxefgzSfapqoMXpt8uyWr7puN/GbDB\neFMCNqvrZOqKcHFV3SrJb65Q5ylVtV9VHZTkiUleP5e/NMlTl65sUVXXq6oHrWel3f2YZVfAWHzc\nZrX5qmqf+ceGV0lylaq6RlXtszC9q+pu62jCy5M8pqr+c02uVVX3rarrrDnn5bfln7azLdfu7n9a\nZVseWlU3mYdvmuTZSU6al/mtJG9O8odz2+6Sqf/7X8/1715VN53bflCS5yZ56462HWB3ErCBzerJ\nSX4tU9eDl+eH4XnRW5N8JMlpSd6e5JVJ0t1vSfK8JK+bu5d8Isl9dnN7n57kO0mOTfLr8/DTk2QO\nmt9M8vG1FtLdpyb5jSQvSvK1JGdmlR8x7ka3TvKBqvpWpkv2fXpu05LHJrlmkgsy/Xj0N7t76Qz2\n7ZN8IMm35uePJ3nCFdRugHWp7t7TbQBgF1TVrye5TXc/dU+3BQABGwAAhtJFBAAABhKwAQBgIAEb\nAAAG2mftKrvf/vvv31u3bt3TzQAAgFV95CMf+Up3b1mr3oYI2Fu3bs2pp566p5sBAACrqqovrqee\nLiIAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnY\nAAAwkIANAAADCdgAADDQPnu6AQCMt/XYt+/wPGc99767oSUAm48z2AAAMJCADQAAAwnYAAAwkIAN\nAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMNCaAbuq\nDqqqk6vqU1X1yap64lz+zKo6t6pOmx+HLczz1Ko6s6o+XVW/sjs3AAAANpJ91lHn0iS/3d0frarr\nJPlIVZ04T3tBd//vxcpVdeskRya5TZIbJ3lXVd2yuy8b2XAAANiI1jyD3d3ndfdH5+FvJjkjyQHb\nmeXwJK/r7ku6+wtJzkxyxxGNBQCAjW6H+mBX1dYkt0/yobno8VV1elW9qqr2m8sOSHL2wmznZIVA\nXlXHVNWpVXXqtm3bdrjhAACwEa07YFfVtZO8KcmTuvsbSV6S5MeTHJLkvCR/siMr7u6Xdfeh3X3o\nli1bdmRWAADYsNYVsKvqqpnC9Wu6+81J0t1f7u7LuvsHSV6eH3YDOTfJQQuzHziXAQDAXm89VxGp\nJK9MckZ3/+lC+Y0Wqj0gySfm4ROSHFlVV6+qmyU5OMk/j2syAABsXOu5ishdkjwsycer6rS57GlJ\nHlJVhyTpJGcleXSSdPcnq+oNST6V6Qokj3MFEQAANos1A3Z3vz9JrTDpHduZ59lJnr0L7QIAgCsl\nd3IEAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjA\nBgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYA\ngIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICB\nBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRs\nAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGGjNgF1VB1XVyVX1qar6ZFU9cS6/flWdWFWfnZ/3m8ur\nql5YVWdW1elVdYfdvREAALBRrOcM9qVJfru7b53kTkkeV1W3TnJskpO6++AkJ83jSXKfJAfPj2OS\nvGR4qwEAYINaM2B393nd/dF5+JtJzkhyQJLDkxw/Vzs+yRHz8OFJXt2TU5LsW1U3Gt5yAADYgHao\nD3ZVbU1y+yQfSnLD7j5vnnR+khvOwwckOXthtnPmsuXLOqaqTq2qU7dt27aDzQYAgI1p3QG7qq6d\n5E1JntTd31ic1t2dpHdkxd39su4+tLsP3bJly47MCgAAG9a6AnZVXTVTuH5Nd795Lv7yUteP+fmC\nufzcJActzH7gXAYAAHu99VxFpJK8MskZ3f2nC5NOSHLUPHxUkrculD98vprInZJctNCVBAAA9mr7\nrKPOXZI8LMnHq+q0uexpSZ6b5A1VdXSSLyZ58DztHUkOS3Jmkm8neeTQFgMAwAa2ZsDu7vcnqVUm\n33OF+p3kcbvYLgAAuFJyJ0cAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYA\ngIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICB\nBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRs\nAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAA\nGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgdYM2FX1qqq6oKo+sVD2zKo6\nt6pOmx+HLUx7alWdWVWfrqpf2V0NBwCAjWg9Z7CPS3LvFcpf0N2HzI93JElV3TrJkUluM8/zF1V1\nlVGNBQCAjW7NgN3d70ty4TqXd3iS13X3Jd39hSRnJrnjLrQPAACuVHalD/bjq+r0uQvJfnPZAUnO\nXqhzzlx2OVV1TFWdWlWnbtu2bReaAQAAG8fOBuyXJPnxJIckOS/Jn+zoArr7Zd19aHcfumXLlp1s\nBgAAbCw7FbC7+8vdfVl3/yDJy/PDbiDnJjlooeqBcxkAAGwKOxWwq+pGC6MPSLJ0hZETkhxZVVev\nqpslOTjJP+9aEwEA4Mpjn7UqVNVrk9wtyf5VdU6SZyS5W1UdkqSTnJXk0UnS3Z+sqjck+VSSS5M8\nrrsv2z1NBwCAjWfNgN3dD1mh+JXbqf/sJM/elUYBAMCVlTs5AgDAQAI2AAAMJGADAMBAAjYAAAwk\nYAMAwEACNgAADCRgAwDAQAI2AAAMJGADAMBAAjYAAAwkYAMAwEACNgAADCRgAwDAQAI2AAAMJGAD\nAMBAAjYAAAwkYAMAwEACNgAADCRgAwDAQAI2AAAMJGADAMBAAjYAAAwkYAMAwEACNgAADCRgAwDA\nQAI2AAAMJGADAMBAAjYAAAwkYAMAwEACNgAADCRgAwDAQAI2AAAMJGADAMBAAjYAAAwkYAMAwEAC\nNgAADCRgAwDAQAI2AAAMJGADAMBAAjYAAAwkYAMAwEACNgAADCRgAwDAQAI2AAAMJGADAMBAawbs\nqnpVVV1QVZ9YKLt+VZ1YVZ+dn/eby6uqXlhVZ1bV6VV1h93ZeAAA2GjWcwb7uCT3XlZ2bJKTuvvg\nJCfN40lynyQHz49jkrxkTDMBAODKYc2A3d3vS3LhsuLDkxw/Dx+f5IiF8lf35JQk+1bVjUY1FgAA\nNrqd7YN9w+4+bx4+P8kN5+EDkpy9UO+cuexyquqYqjq1qk7dtm3bTjYDAAA2ll3+kWN3d5Leifle\n1t2HdvehW7Zs2dVmAADAhrCzAfvLS10/5ucL5vJzkxy0UO/AuQwAADaFnQ3YJyQ5ah4+KslbF8of\nPl9N5E5JLlroSgIAAHu9fdaqUFWvTXK3JPtX1TlJnpHkuUneUFVHJ/likgfP1d+R5LAkZyb5dpJH\n7oY2AwDAhrVmwO7uh6wy6Z4r1O0kj9vVRgEAwJWVOzkCAMBAAjYAAAwkYAMAwEACNgAADCRgAwDA\nQAI2AAAMJGADAMBAAjYAAAwkYAMA/P/t3W2sZPVdB/DvTzbQ+Fi21ZVWw0JCrSRG2mywUWOlbehT\nAhgpbpPGrWKw9eGNMXEb3hgTI/imibFJJU0taqTUNaRrsCKP8U2pxaQt0AosFGNXyiJ9iI1xBfr3\nxZyrx2Uud+69/7kz987nk0zmPM78zy//c+53zj0zBzoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErAB\nAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCg\nIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMB\nGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsA\nADoSsAEAoKN921m5qp5M8h9JXkjyfGvtUFXtT3JrkoNJnkxyTWvt69trJgAA7A49zmBf1lq7pLV2\naBg/muTu1tpFSe4exgEAYCXM4xKRK5PcPAzfnOSqObwHAAAspe0G7Jbk76vqn6rqumHagdbaU8Pw\nV5McmLZiVV1XVQ9U1QPPPPPMNpsBAADLYVvXYCf56dbayar6gSR3VtU/j2e21lpVtWkrttZuSnJT\nkhw6dGjqMgAAsNts6wx2a+3k8HwqyW1JLk3ydFWdlyTD86ntNhIAAHaLLQfsqvquqvqeteEklyd5\nKMnxJEeGxY4k+eR2GwkAALvFdi4ROZDktqpae52/bK39XVV9NsknquraJP+S5JrtNxMAAHaHLQfs\n1toTSX58yvRnk7x5O40CAIDdyp0cAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCg\nIwEbAAA6ErABAKAjARsAADoSsAEAoCMBGwAAOhKwAQCgIwEbAAA6ErABAKAjARsAADoSsAEAoCMB\nGwAAOhKwAQCgIwEbAAA62rfoBgAAsJoOHr190+s8ecM759CSvpzBBgCAjgRsAADoSMAGAICOBGwA\nAOhIwAYAgI4EbAAA6EjABgCAjgRsAADoSMAGAICOBGwAAOhIwAYAgI4EbAAA6EjABgCAjgRsAADo\naN+iG7BoB4/evul1nrzhnXNoCQAAe4Ez2AAA0JGADQAAHQnYAADQkYANAAAdCdgAANCRgA0AAB0J\n2AAA0JGADQAAHQnYAADQkYANAAAdzS1gV9XbquqRqjpRVUfn9T4AALBM5hKwq+qsJB9K8vYkFyd5\nd1VdPI/3AgCAZTKvM9iXJjnRWnuitfbfST6e5Mo5vRcAACyNfXN63Vcn+dfR+FeS/MR4gaq6Lsl1\nw+i3quqRObVlI69M8u+bWaFunFNLdpdN140k6rZV6rZ5jm1bo69tjbptjbptQd240LqdP8tC8wrY\nG2qt3ZTkpkW9/5qqeqC1dmjR7dht1G1r1G1r1G3z1Gxr1G1r1G1r1G1rdkPd5nWJyMkkPzwa/6Fh\nGgAA7GnzCtifTXJRVV1QVWcnOZzk+JzeCwAAlsZcLhFprT1fVb+R5I4kZyX5aGvt4Xm8VwcLv0xl\nl1K3rVG3rVG3zVOzrVG3rVG3rVG3rVn6ulVrbdFtAACAPcOdHAEAoCMBGwAAOlqJgF1V76qqh6vq\n21W17s+6rHd79+HLmp8Zpt86fHFzz6uq/VV1Z1U9NjyfO2WZy6rqc6PHf1XVVcO8j1XVl0fzLtn5\nrdh5s9RtWO6FUW2Oj6avXH+bsa9dUlWfHvblL1TVL4zmrVRfW+9YNZp/ztB3Tgx96eBo3geG6Y9U\n1Vt3st2LNkPdfquqvjj0r7ur6vzRvKn76yqYoW7vrapnRvX5ldG8I8N+/VhVHdnZli/ODDX74Khe\nj1bVN0bzVrmvfbSqTlXVQ+vMr6r6o6GuX6iq14/mLVdfa63t+UeSH03yI0nuS3JonWXOSvJ4kguT\nnJ3k80kuHuZ9IsnhYfjDSd6/6G3aobr9YZKjw/DRJDdusPz+JF9L8p3D+MeSXL3o7VjWuiX51jrT\nV66/zVKzJK9JctEw/KokTyV5+TC+Mn3tpY5Vo2V+LcmHh+HDSW4dhi8elj8nyQXD65y16G1aorpd\nNjp+vX+tbsP41P11rz9mrNt7k/zxlHX3J3lieD53GD530du0DDU7Y/nfzOTHIFa6rw3b/jNJXp/k\noXXmvyPJp5JUkjck+cwwfen62kqcwW6tfam1ttGdIqfe3r2qKsmbkhwblrs5yVXza+1SuTKT7U1m\n2+6rk3yqtfafc23V8tts3f7XCve3DWvWWnu0tfbYMPxvSU4l+f4da+HymHqsOmOZcT2PJXnz0Leu\nTPLx1trp1tqXk5wYXm8VbFi31tq9o+PX/Zncw2HVzdLf1vPWJHe21r7WWvt6kjuTvG1O7Vwmm63Z\nu5PcsiMtW3KttX/I5ETdeq5M8mdt4v4kL6+q87KEfW0lAvaMpt3e/dVJXpHkG62158+YvgoOtNae\nGoa/muTABssfzosPEr8//Bvng1V1TvcWLqdZ6/ayqnqgqu5fu6wmq9vfNtXXqurSTM4MPT6avCp9\nbb1j1dRlhr70zUz61izr7lWb3fZrMzlTtmba/roKZq3bzw/737GqWrvR3Kr2t5m3e7gM6YIk94wm\nr2pfm8V6tV26vrawW6X3VlV3JfnBKbOub619cqfbs1u8VN3GI621VlXr/qbj8AnyxzL57fM1H8gk\nLJ2dyW9W/k6S39tum5dBp7qd31o7WVUXJrmnqh7MJAjtSZ372p8nOdJa+/Ywec/2NXZeVb0nyaEk\nbxxNftH+2lp7fPorrJy/SXJLa+10Vf1qJv89edOC27RbHE5yrLX2wmiavrYH7JmA3Vp7yzZfYr3b\nuz+byb8g9g1ngvbUbd9fqm5V9XRVnddae2oINade4qWuSXJba+250WuvnZE8XVV/muS3uzR6CfSo\nW2vt5PD8RFXdl+R1Sf46e7S/9ahZVX1vktsz+eB8/+i192xfm2K9Y9W0Zb5SVfuSfF8mx7JZ1t2r\nZtr2qnpLJh/63thaO702fZ39dRVCz4Z1a609Oxr9SCbfqVhb92fPWPe+7i1cPpvZzw4n+fXxhBXu\na7NYr7ZL19dcIvJ/pt7evU2unr83k+uLk+RIklU5I348k+1NNt7uF11DNgSlteuKr0oy9VvBe9CG\ndauqc9cuY6iqVyb5qSRfXOH+NkvNzk5yWybX3x07Y94q9bWpx6ozlhnX8+ok9wx963iSwzX5lZEL\nklyU5B93qN2LtmHdqup1Sf4kyRWttVOj6VP31x1r+WLNUrfzRqNXJPnSMHxHksuH+p2b5PL8//9y\n7lWz7KOpqtdm8oW8T4+mrXJfm8XxJL84/JrIG5J8czjBsnx9bZHfsNypR5Kfy+R6nNNJnk5yxzD9\nVUn+drTcO5I8msknxetH0y/M5I/QiSR/leScRW/TDtXtFUnuTvJYkruS7B+mH0rykdFyBzP59Pgd\nZ6x/T5IHMwk7f5Hkuxe9TctStyQ/OdTm88Pztavc32as2XuSPJfkc6PHJavY16YdqzK5JOaKYfhl\nQ985MfSlC0frXj+s90iSty96W5asbncNfyPW+tfxYfq6++sqPGao2x8keXioz71JXjta95eHfngi\nyS8teluWpWbD+O8mueGM9Va9r92SyS9EPZdJbrs2yfuSvG+YX0k+NNT1wYx+GW7Z+ppbpQMAQEcu\nEQEAgI4EbAAA6EjABtpCVkoAAAAfSURBVACAjgRsAADoSMAGAICOBGwAAOhIwAYAgI7+B5cKo4Mf\n/nxvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "xI5WXfeQ8jx8"
},
"source": [
"## Experiment 3: Using SentiWord to enrich data\n",
"\n",
"**Hypothesis**: The sentiment of a sentence can be determined by the number of times the word occurs with that rating **in the SentiWord dataset**.\n",
"\n",
"**Approach**:\n",
"Basic approach is as follows:\n",
"1. Convert sentiWord to a dict with scores.\n",
"1. For each sentence, compute the 'average rating' by looking up the average rate from the dict.\n",
"\n",
"**Observations**:\n",
"1. Precision = **0.645**\n",
"1. Recall = **0.645**\n",
"1. F_Measure = **0.644**\n",
"\n",
"**Conclusion**: Better than before as words have a negative and psitive \"intensity\" but still, most words are classified into the 'neutral' category. We lose info from that."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "EiemsHM88jx9"
},
"source": [
"### Converting SentiWord"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "6GMeoBwJ8jx-",
"colab": {}
},
"source": [
"sen_dict = defaultdict(float, dict(zip(sen_wordDF['SynsetTerm'], sen_wordDF['Score'])))\n",
"# sen_dict"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "wQPq5ZEd8jyE"
},
"source": [
"### Calculating metrics"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "d_KFPhRt8jyF",
"outputId": "1e07b577-8c16-452e-de5e-47cdcbaf4f1d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"def test_n_fold_3(testingDF, num_folds = 10):\n",
" cum_model_precision = float()\n",
" cum_model_recall = float()\n",
" cum_model_f = float()\n",
"\n",
" featureDF = testingDF.sample(frac=1)\n",
"\n",
" results = test_df(lookup_sentiment(featureDF, sen_dict))\n",
"\n",
" cum_model_precision = round(results[0][0], 3)\n",
" cum_model_recall = round(results[0][1], 3)\n",
" cum_model_f = round(results[0][2], 3)\n",
" print(('final precision', 'final recall', 'final f_measure'), ' = ', (cum_model_precision, cum_model_recall, cum_model_f))\n",
"\n",
"\n",
"\n",
"for key, value in (zip([\"vanilla\", \"with negation & no stopwords\",\"no stopwords\"], [movieDF, movieDF2, movieDF3])):\n",
" print(key)\n",
"# print(\"vanilla\")\n",
" test_n_fold_3(value)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"vanilla\n",
"('final precision', 'final recall', 'final f_measure') = (0.622, 0.622, 0.622)\n",
"with negation & no stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.559, 0.561, 0.556)\n",
"no stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.645, 0.645, 0.644)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "DX-b2PJ08jyH"
},
"source": [
"### Explaining results\n",
"We have taken care of the problem of having bad data for a lot of dict entries but we are still losing a lot of info as most words don't contribute to calculating the sentiment."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "eg3gqeSD8jyH",
"outputId": "ff067ba4-e6e6-48a3-f56f-8ffcee47d3c7",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"# no more bad learnings :)\n",
"sen_dict['disgust'], sen_dict['shit'], sen_dict['okay'], sen_dict['like']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(-0.125, -0.25, 0.0, 0.125)"
]
},
"metadata": {
"tags": []
},
"execution_count": 36
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "C3IHgRHb8jyL",
"outputId": "c3712316-4c6d-4382-b645-44b60cbb5fa7",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"get_random = lambda obj: obj.loc[np.random.choice(obj.index, 1, True),:]\n",
"reviews = featureDF.groupby('label').apply(get_random)['review'].tolist()\n",
"# reviews = featureDF.groupby('label').head(1).sort_values('label')['review'].tolist()\n",
"\n",
"fig, ax = plt.subplots(len(reviews),1, figsize=(10,10), constrained_layout=True)\n",
"\n",
"for i in range(0, len(reviews)):\n",
" review = reviews[i]\n",
" ax[i].hist([sen_dict[word] for word in review], bins=50)\n",
" ax[i].set_title(str(\"label = \" + str(2*i - 1)) + \", len = \" + str(len(review)))\n",
"\n",
"fig.suptitle('Sentiment of each word in ' + str(len(reviews)) + ' sample labels', fontsize=16)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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UfAU4tKp+cWxX1duTXMJwu70/Y8gHaxm6tczU9eUnDHev+QuGLis/B74NPLmqTpzLNkmb\nm9z6onNJkjSuda34LvAXVdXrEfKSNkNe5Stps5fhCYiPmLBuJbnnBq5ng+e9LSR5WJJLZ68pSZoP\nA7YkbUKS/EaST2V4NPwm/xVkkv0zPMr9B0kuTLJiZNquSVZleAx3Zexx5El2S3JihseaX5rpn2Io\nSbc5A7YkbVpuYuhffPjGbsh8ZXi0+IcZbk94F4Z7Kv9bkvu3Kj9nuPvGH8+wiPcwdNnYBXgM8Jok\nD1+o9lbVRVUVu4dImo0BW9IWJcm+Sb6U5NoklyV5c5Ktx6od1M6mXpnk9aMPzUjyzCTnJbmmnUne\n87Zsf1V9u6qOYfr7Fc9Jkrsl+WCSdUm+m+T5I9NemeT4JMcl+WGSb7SL9HrakeGOPe+uwVeA84Bf\nB6iq71fVWxguyhtv+9T9sP+5qm5qF8uewHBBqCRtVAZsSVuanwF/zXA3hQcx3DbvOWN1/ghYznDH\ni4NpoS3JwcDLgMcz3AXi8wy3nptVkre0UD/d65wO2zUn7UPDRxlum7cbw354YZI/GKn2OOD9wPYM\nd2F583qWd856tu8t081TVd9n2H/PSLJVkqkHqHxhuvrjqxx7nxr2/suSNjoDtqQtSlWdVVVnVNXN\n7b7db2e41++oI6vq6nb/8zcw3AIOhvuiv7aqzquqmxlut7bPJGexq+o5VbX9DK/7zTb/AvhtYGlV\n/WNV/bSqLmS47eKhI3W+UFUfr6qfAe9meDT3tKrqfuvZvvEPMKPex3CP6BsZPrC8vKouma3x7YFF\n/w38fZJtkzyQoSvJ+FM6Jek2Z8CWtEVJcq8kJyW5PMkPGELyzmPVRgPexcDd2vCewBunzswy3CM7\nLNADTZI8Ocn17fWJzovfE7jb6JlmhrPzo4/Vvnxk+AZg28z8OO45S3IfhjPkT2N4CM59gb9L8pgJ\nF/FkhvvGX8JwD+z3cMsndErSRmHAlrSleSvDU+f2rqo7M4TKjNXZY2T47vzyseKXAM8aOzt7h6r6\n4mwrTfK2kbA8/pq2P3VVvbeq7tRej57zlq7fJcB3x7Zlu6o6aEMW1vpoz7R9b5thtt8AvlNVn6qq\nn7dHbX8MmGhbq+riqnpsVS2tqt9h+KD05Q1pvyT1ZMCWtKXZjuEx0de3M6h/OU2dFyfZIckeDE8D\n/EArfxvw0iT3heFR0UmeOMlKq+rZI2F5/HXfSRufwbYMZ3xp3SO2GZl+bJJjJ1jUl4EfJnlJkju0\nPtC/keS3J23LqKq673q2b6bb530V2Lvdqi9J/g/wWOAXfdLbtk5t3zZtfGraryXZLsnWSZ4CPAr4\ntw1pvyT1ZMCWtKV5EcOjoH/I0Of4A9PUORE4i+ER0R8DjgGoqg8zPGr8/a17ydeZ8GxrR3syPAp8\n6qz3jxkeTz1lD279KPJbaf2qHwvsw3CruyuBdzDcLu82UVUXMFxA+iaGDz2fBT7Y2jHlx8D1bfhb\nbXzKHzA8Sv4ahv7xB1bVugVutiTNykelS9Jmot1u8GvA/arqpo3dHknaUhmwJUmSpI7sIiJJkiR1\nZMCWJEmSOjJgS5IkSR11e2DAfOy88861bNmyjd0MSZIkaUZnnXXWlVW1dLZ6iyJgL1u2jNWrV2/s\nZkiSJEkzSnLxJPXsIiJJkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJ\nktSRAVuSJEnqyIAtSZIkdWTAliRJkjoyYEuSJEkdGbAlSZKkjpZs7AZIkma37IiPzan+Ra97zAK1\nRJI0G89gS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuSJEnqyIAtSZIkdWTAliRJ\nkjoyYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmSJKkjA7YkSZLUkQFbkiRJ6siALUmSJHVkwJYkSZI6\nMmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1NFEATvJ9klOSPKtJOcleVCSHZOcnOT8\n9r5Dq5skb0qyJsk5SR64sJsgSZIkLR6TnsF+I/DJqroPcH/gPOAI4JSq2hs4pY0DPBrYu71WAG/t\n2mJJkiRpEZs1YCe5C/D7wDEAVfXTqroWOBhY2aqtBA5pwwcDx9XgDGD7JLt2b7kkSZK0CE1yBnsv\nYB3wriRfTfKOJHcEdqmqy1qdy4Fd2vBuwCUj81/aym4hyYokq5OsXrdu3YZvgSRJkrSITBKwlwAP\nBN5aVQ8AfsQvu4MAUFUF1FxWXFVHV9Xyqlq+dOnSucwqSZIkLVqTBOxLgUur6sw2fgJD4P7+VNeP\n9n5Fm74W2GNk/t1bmSRJkrTZmzVgV9XlwCVJ7t2KDgC+CawCDmtlhwEntuFVwNPa3UT2A64b6Uoi\nSZIkbdaWTFjvr4D3JtkauBB4BkM4Pz7J4cDFwJNa3Y8DBwFrgBtaXUmSJGmLMFHArqqzgeXTTDpg\nmroFPHee7ZIkSZI2ST7JUZIkSerIgC1JkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZs\nSZIkqSMDtiRJktSRAVuSJEnqyIAtSZIkdWTAliRJkjoyYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmS\nJKkjA7YkSZLUkQFbkiRJ6siALUmSJHVkwJYkSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSp\nIwO2JEmS1JEBW5IkSerIgC1JkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMD\ntiRJktSRAVuSJEnqyIAtSZIkdWTAliRJkjqaKGAnuSjJuUnOTrK6le2Y5OQk57f3HVp5krwpyZok\n5yR54EJugCRJkrSYzOUM9sOrap+qWt7GjwBOqaq9gVPaOMCjgb3bawXw1l6NlSRJkha7+XQRORhY\n2YZXAoeMlB9XgzOA7ZPsOo/1SJIkSZuMSQN2AZ9OclaSFa1sl6q6rA1fDuzShncDLhmZ99JWdgtJ\nViRZnWT1unXrNqDpkiRJ0uKzZMJ6D6mqtUl+FTg5ybdGJ1ZVJam5rLiqjgaOBli+fPmc5pUkSZIW\nq4nOYFfV2vZ+BfBhYF/g+1NdP9r7Fa36WmCPkdl3b2WSJEnSZm/WgJ3kjkm2mxoGHgV8HVgFHNaq\nHQac2IZXAU9rdxPZD7hupCuJJEmStFmbpIvILsCHk0zV/8+q+mSSrwDHJzkcuBh4Uqv/ceAgYA1w\nA/CM7q2WJEmSFqlZA3ZVXQjcf5ryq4ADpikv4LldWidJkiRtYnySoyRJktSRAVuSJEnqyIAtSZIk\ndWTAliRJkjoyYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmSJKkjA7YkSZLUkQFbkiRJ6siALUmSJHVk\nwJYkSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerIgC1JkiR1ZMCW\nJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuSJEnqyIAtSZIkdWTAliRJ\nkjoyYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmSJKkjA7YkSZLUkQFbkiRJ6siALUmSJHVkwJYkSZI6\nmjhgJ9kqyVeTnNTG90pyZpI1ST6QZOtWvk0bX9OmL1uYpkuSJEmLz1zOYL8AOG9k/EjgqKq6J3AN\ncHgrPxy4ppUf1epJkiRJW4SJAnaS3YHHAO9o4wH2B05oVVYCh7Thg9s4bfoBrb4kSZK02Zv0DPYb\ngL8Dft7GdwKuraqb2/ilwG5teDfgEoA2/bpW/xaSrEiyOsnqdevWbWDzJUmSpMVl1oCd5LHAFVV1\nVs8VV9XRVbW8qpYvXbq056IlSZKkjWbJBHUeDDwuyUHAtsCdgTcC2ydZ0s5S7w6sbfXXAnsAlyZZ\nAtwFuKp7yyVJkqRFaNYz2FX10qravaqWAYcCp1bVk4HTgCe0aocBJ7bhVW2cNv3UqqqurZYkSZIW\nqfncB/slwN8kWcPQx/qYVn4MsFMr/xvgiPk1UZIkSdp0TNJF5Beq6nTg9DZ8IbDvNHV+AjyxQ9sk\nSZKkTY5PcpQkSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerIgC1J\nkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuSJEnqyIAtSZIk\ndWTAliRJkjoyYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmSJKkjA7YkSZLUkQFbkiRJ6siALUmSJHVk\nwJYkSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerIgC1JkiR1ZMCW\nJEmSOjJgS5IkSR0ZsCVJkqSOZg3YSbZN8uUkX0vyjSSvauV7JTkzyZokH0iydSvfpo2vadOXLewm\nSJIkSYvHJGewbwT2r6r7A/sABybZDzgSOKqq7glcAxze6h8OXNPKj2r1JEmSpC3CrAG7Bte30du3\nVwH7Aye08pXAIW344DZOm35AknRrsSRJkrSITdQHO8lWSc4GrgBOBi4Arq2qm1uVS4Hd2vBuwCUA\nbfp1wE7TLHNFktVJVq9bt25+WyFJkiQtEhMF7Kr6WVXtA+wO7AvcZ74rrqqjq2p5VS1funTpfBcn\nSZIkLQpzuotIVV0LnAY8CNg+yZI2aXdgbRteC+wB0KbfBbiqS2slSZKkRW6Su4gsTbJ9G74D8Ejg\nPIag/YRW7TDgxDa8qo3Tpp9aVdWz0ZIkSdJitWT2KuwKrEyyFUMgP76qTkryTeD9SV4NfBU4ptU/\nBnh3kjXA1cChC9BuSZIkaVGaNWBX1TnAA6Ypv5ChP/Z4+U+AJ3ZpnSRJkrSJ8UmOkiRJUkcGbEmS\nJKkjA7YkSZLUkQFbkiRJ6siALUmSJHVkwJYkSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSp\nIwO2JEmS1JEBW5IkSerIgC1JkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMD\ntiRJktSRAVuSJEnqyIAtSZIkdWTAliRJkjoyYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmSJKkjA7Yk\nSZLUkQFbkiRJ6siALUmSJHVkwJYkSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS\n1NGsATvJHklOS/LNJN9I8oJWvmOSk5Oc3953aOVJ8qYka5Kck+SBC70RkiRJ0mIxyRnsm4G/rapf\nB/YDnpvk14EjgFOqam/glDYO8Ghg7/ZaAby1e6slSZKkRWrWgF1Vl1XV/7ThHwLnAbsBBwMrW7WV\nwCFt+GDguBqcAWyfZNfuLZckSZIWoTn1wU6yDHgAcCawS1Vd1iZdDuzShncDLhmZ7dJWNr6sFUlW\nJ1m9bt26OTZbkiRJWpwmDthJ7gR8EHhhVf1gdFpVFVBzWXFVHV1Vy6tq+dKlS+cyqyRJkrRoTRSw\nk9yeIVy/t6o+1Iq/P9X1o71f0crXAnuMzL57K5MkSZI2e5PcRSTAMcB5VfVvI5NWAYe14cOAE0fK\nn9buJrIfcN1IVxJJkiRps7ZkgjoPBp4KnJvk7Fb2MuB1wPFJDgcuBp7Upn0cOAhYA9wAPKNriyVJ\nkqRFbNaAXVVfADLD5AOmqV/Ac+fZLkmSJGmT5JMcJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEB\nW5IkSerIgC1JkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuS\nJEnqyIAtSZIkdWTAliRJkjoyYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmSJKkjA7YkSZLUkQFbkiRJ\n6siALUmSJHVkwJYkSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerI\ngC1JkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktTRrAE7yTuTXJHk\n6yNlOyY5Ocn57X2HVp4kb0qyJsk5SR64kI2XJEmSFptJzmAfCxw4VnYEcEpV7Q2c0sYBHg3s3V4r\ngLf2aaYkSZK0aZg1YFfV54Crx4oPBla24ZXAISPlx9XgDGD7JLv2aqwkSZK02G1oH+xdquqyNnw5\nsEsb3g24ZKTepa3sVpKsSLJ9uexjAAAgAElEQVQ6yep169ZtYDMkSZKkxWXeFzlWVQG1AfMdXVXL\nq2r50qVL59sMSZIkaVHY0ID9/amuH+39ila+FthjpN7urUySJEnaImxowF4FHNaGDwNOHCl/Wrub\nyH7AdSNdSSRJkqTN3pLZKiR5H/AwYOcklwL/ALwOOD7J4cDFwJNa9Y8DBwFrgBuAZyxAmyVJkqRF\na9aAXVV/OsOkA6apW8Bz59soSZIkaVPlkxwlSZKkjgzYkiRJUkcGbEmSJKkjA7YkSZLUkQFbkiRJ\n6siALUmSJHVkwJYkSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerI\ngC1JkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktTRko3dAElabJYd\n8bE51b/odY9ZoJZIkjZFnsGWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSR\nt+mTJN1mvAWipC2BZ7AlSZKkjgzYkiRJUkd2EZEkaY7s6iJpfTyDLUmSJHXkGWxJkrRo+O2ANgcG\nbEnd+I9RkiQDthbQXMMWGLgm5b6VpNuOJw80VwsSsJMcCLwR2Ap4R1W9biHWI0mSBoZAafHoHrCT\nbAX8O/BI4FLgK0lWVdU3e69L2hD+E5IkbYn8/3fbWYgz2PsCa6rqQoAk7wcOBhZlwPardkmSJPWU\nquq7wOQJwIFV9edt/KnA71TV88bqrQBWtNF7A9/u2pDN287AlRu7EZs59/HCcv8uPPfxwnMfLzz3\n8cJzH8/NnlW1dLZKG+0ix6o6Gjh6Y61/U5ZkdVUt39jt2Jy5jxeW+3fhuY8Xnvt44bmPF577eGEs\nxINm1gJ7jIzv3sokSZKkzd5CBOyvAHsn2SvJ1sChwKoFWI8kSZK06HTvIlJVNyd5HvAphtv0vbOq\nvtF7PVs4u9YsPPfxwnL/Ljz38cJzHy889/HCcx8vgO4XOUqSJElbsoXoIiJJkiRtsQzYkiRJUkcG\n7EUoyY5JTk5yfnvfYZo6D09y9sjrJ0kOadOOTfLdkWn73PZbsbhNso9bvZ+N7MdVI+V7JTkzyZok\nH2gX9GrEhMfxPkm+lOQbSc5J8icj0zyOZ5DkwCTfbsffEdNM36Ydl2vacbpsZNpLW/m3k/zBbdnu\nTckE+/hvknyzHbenJNlzZNq0fzd0SxPs46cnWTeyL/98ZNph7W/L+UkOu21bvumYYB8fNbJ/v5Pk\n2pFpHsfzYB/sRSjJvwBXV9Xr2i/EDlX1kvXU3xFYA+xeVTckORY4qapOuG1avOmZdB8nub6q7jRN\n+fHAh6rq/UneBnytqt668C3fdEyyj5PcC6iqOj/J3YCzgF+rqms9jqeXZCvgO8AjgUsZ7tz0p1X1\nzZE6zwHuV1XPTnIo8EdV9SdJfh14H8MTd+8GfAa4V1X97LbejsVswn38cODM9jf3L4GHVdWftGnT\n/t3QL024j58OLJ/mQXU7AquB5UAx/N34raq65rZp/aZhkn08Vv+vgAdU1TPbuMfxPHgGe3E6GFjZ\nhlcCh8xS/wnAJ6rqhgVt1eZlrvv4F5IE2B+YCn5zmn8LMus+rqrvVNX5bfh7wBXArE/I2sLtC6yp\nqgur6qfA+xn29ajRfX8CcEA7bg8G3l9VN1bVdxk+mO97G7V7UzLrPq6q00b+5p7B8MwHTW6S43gm\nfwCcXFVXt1B9MnDgArVzUzbXffynDB/A1YEBe3Hapaoua8OXA7vMUv9Qbv1L8c/tq8ujkmzTvYWb\nvkn38bZJVic5Y6oLDrATcG1V3dzGLwV2W8C2bqrmdBwn2RfYGrhgpNjj+NZ2Ay4ZGZ/u+PtFnXac\nXsdw3E4yr+a+nw4HPjEyPt3fDd3SpPv4j9vfgBOSTD3EzuN4MhPvp9bFaS/g1JFij+N52GiPSt/S\nJfkMcNdpJr18dKSqKsmM/XiS7Ar8JsN9x6e8lCHQbM1wf8uXAP843zZvajrt4z2ram2SewCnJjmX\nIayI7sfxu4HDqurnrdjjWItekqcwdFV46Ejxrf5uVNUF0y9B6/FR4H1VdWOSZzF8K7P/Rm7T5upQ\n4ISx7mIex/NgwN5IquoRM01L8v0ku1bVZS14XLGeRT0J+HBV3TSy7KmzhjcmeRfwoi6N3sT02MdV\ntba9X5jkdOABwAeB7ZMsaWcHdwfWdt+ATUCPfZzkzsDHgJdX1Rkjy/Y4nt5aYI+R8emOv6k6lyZZ\nAtwFuGrCeTXhfkryCIYPkw+tqhunymf4u2EwuaVZ93FVXTUy+g7gX0bmfdjYvKd3b+Gmby6/74cC\nzx0t8DieH7uILE6rgKmrog8DTlxP3Vv1mWphZqqv8CHA1xegjZu6Wfdxkh2muiUk2Rl4MPDNGq4M\nPo2h7/uM82uifbw18GHguPGLGT2OZ/QVYO8Md7LZmuEf4/gV/qP7/gnAqe24XQUcmuEuI3sBewNf\nvo3avSmZdR8neQDwduBxVXXFSPm0fzdus5ZvOibZx7uOjD4OOK8Nfwp4VNvXOwCP4pbf4mowyd8K\nktwH2AH40kiZx/F8VZWvRfZi6Ct5CnA+w1X+O7by5cA7RuotY/g0erux+U8FzmUIJO8B7rSxt2mx\nvSbZx8Dvtv34tfZ++Mj892AIJmuA/wK22djbtNheE+7jpwA3AWePvPZp0zyOZ963BzHcHeAChjP/\nMHSfeVwb3rYdl2vacXqPkXlf3ub7NvDojb0ti/U1wT7+DPD9keN2VSuf8e+Grznv49cC32j78jTg\nPiPzPrMd32uAZ2zsbVmsr9n2cRt/JfC6sfk8juf58jZ9kiRJUkd2EZEkSZI6MmBLkiRJHRmwJUmS\npI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerIgC1JkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSO\nDNiSJElSRwZsSZIkqSMDtiRJktSRAVvSFiPJRUkeMWHdSnLPDVzPBs+7EJIcm+TVG7sdkrSlMGBL\n0iYgyfOSrE5yY5JjN3Z75ivJPZKclOSHSa5M8i8j05Yl+XiSa5JcnuTNSZa0ab+X5PqxVyX54423\nNZJ0SwZsSdo0fA94NfDOjd2Q+UqyNXAycCpwV2B34D0jVd4CXAHsCuwDPBR4DkBVfb6q7jT1Ah4L\nXA988rbbAklaPwO2pC1Skn2TfCnJtUkua2dJtx6rdlCSC9sZ1tcnud3I/M9Mcl47y/qpJHsuZHur\n6kNV9RHgqvkuK8ljk5zdtv2LSe43Mu2iJC9Kck6S65J8IMm2813nmKcD36uqf6uqH1XVT6rqnJHp\newHHt/LLGcLzfWdY1mHACVX1o85tlKQNZsCWtKX6GfDXwM7Ag4ADaGdJR/wRsBx4IHAw8EyAJAcD\nLwMeDywFPg+8b5KVJnlLC7bTvc6ZfQnzk+QBDGfBnwXsBLwdWJVkm5FqTwIOZAi692MIxNMt6yHr\n2ZZrkzxkhmbsB1yU5BPtw8vpSX5zZPobgEOT/EqS3YBHM80Z6iR3BJ4ArJzDLpCkBWfAlrRFqqqz\nquqMqrq5qi5iCJoPHat2ZFVdXVX/yxD6/rSVPxt4bVWdV1U3A68B9pnkLHZVPaeqtp/hdb/Z5u9g\nBfD2qjqzqn5WVSuBGxlC75Q3VdX3qupq4KMM3TRupaq+sJ5t2b6qvjBDG3YHDgXeBNwN+Bhw4sg3\nCJ9jOGP9A+BSYDXwkWmW83jgSuCzc9h+SVpwBmxJW6Qk92oX2V2e5AcMIXnnsWqXjAxfzBAGAfYE\n3jh1pha4Ggiw20K3u4M9gb8dPdMM7MEvtw3g8pHhG4A7dW7Dj4EvVNUnquqnwP9jOJv+a60bzieB\nDwF3ZPiZ7AAcOc1yDgOOq6rq3D5JmhcDtqQt1VuBbwF7V9WdGbp8ZKzOHiPDd2e40BCG4P2ssbO1\nd6iqL8620iRvm+YuGFOvb3TYrtlcAvzzWNt/paom6uIyaoY7eoy+fm+GWc8BZgrFOzLs6zdX1Y1V\ndRXwLuCgsXXvATwMOG6u7ZakhWbAlrSl2o6hC8L1Se4D/OU0dV6cZIcW5l4AfKCVvw14aZL7AiS5\nS5InTrLSqnr26F0wxl4zXchHkiXtYsOtgK2SbDt167o2vZI8bIIm/Afw7CS/k8EdkzwmyXaTtH9s\nWz6/nm25U1V9foZZ3wPsl+QRSbYCXsjQ1eO8qroS+C7wl22bt2c4Uz3eP/2pwBer6oK5tluSFpoB\nW9KW6kXAnwE/ZAidH5imzonAWcDZDP2EjwGoqg8zdFl4f+te8nWGC/EW0isYulYcATylDb8CfnE2\n94fAubMtpKpWA38BvBm4BljDDBcxLpSq+jbDNrytteFg4HGtuwgMfasPBNa19t3EcEHqqKfhxY2S\nFqnYdU2SNm1JngLct6peurHbIkkyYEuSJEld2UVEkiRJ6siALUmSJHVkwJYkSZI6WjJ7lYW38847\n17JlyzZ2MyRJkqQZnXXWWVdW1dLZ6i2KgL1s2TJWr169sZshSZIkzSjJxZPUs4uIJEmS1JEBW5Ik\nSerIgC1JkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuSJEnq\nyIAtSZIkdWTAliRJkjoyYEuSJEkdLdnYDZAkLQ7LjvjYnOpf9LrHLFBLJGnT5hlsSZIkqSMDtiRJ\nktSRAVuSJEnqyIAtSZIkdWTAliRJkjoyYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmSJKkjA7YkSZLU\nkQFbkiRJ6siALUmSJHVkwJYkSZI6MmBLkiRJHU0UsJNclOTcJGcnWd3KdkxycpLz2/sOrTxJ3pRk\nTZJzkjxwITdAkiRJWkzmcgb74VW1T1Utb+NHAKdU1d7AKW0c4NHA3u21Anhrr8ZKkiRJi918uogc\nDKxswyuBQ0bKj6vBGcD2SXadx3okSZKkTcakAbuATyc5K8mKVrZLVV3Whi8HdmnDuwGXjMx7aSu7\nhSQrkqxOsnrdunUb0HRJkiRp8VkyYb2HVNXaJL8KnJzkW6MTq6qS1FxWXFVHA0cDLF++fE7zSpIk\nSYvVRGewq2pte78C+DCwL/D9qa4f7f2KVn0tsMfI7Lu3MkmSJGmzN2vATnLHJNtNDQOPAr4OrAIO\na9UOA05sw6uAp7W7iewHXDfSlUSSJEnarE3SRWQX4MNJpur/Z1V9MslXgOOTHA5cDDyp1f84cBCw\nBrgBeEb3VkuSJEmL1KwBu6ouBO4/TflVwAHTlBfw3C6tkyRJkjYxPslRkiRJ6siALUmSJHVkwJYk\nSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerIgC1JkiR1ZMCWJEmS\nOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuSJEnqyIAtSZIkdWTAliRJkjoy\nYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmSJKkjA7YkSZLUkQFbkiRJ6siALUmSJHVkwJYkSZI6MmBL\nkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerIgC1JkiR1ZMCWJEmSOjJgS5Ik\nSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuSJEnqyIAtSZIkdWTAliRJkjoyYEuSJEkd\nGbAlSZKkjgzYkiRJUkcTB+wkWyX5apKT2vheSc5MsibJB5Js3cq3aeNr2vRlC9N0SZIkafGZyxns\nFwDnjYwfCRxVVfcErgEOb+WHA9e08qNaPUmSJGmLMFHATrI78BjgHW08wP7ACa3KSuCQNnxwG6dN\nP6DVlyRJkjZ7k57BfgPwd8DP2/hOwLVVdXMbvxTYrQ3vBlwC0KZf1+rfQpIVSVYnWb1u3boNbL4k\nSZK0uMwasJM8Friiqs7queKqOrqqllfV8qVLl/ZctCRJkrTRLJmgzoOBxyU5CNgWuDPwRmD7JEva\nWerdgbWt/lpgD+DSJEuAuwBXdW+5JEmStAjNega7ql5aVbtX1TLgUODUqnoycBrwhFbtMODENryq\njdOmn1pV1bXVkiRJ0iI1n/tgvwT4myRrGPpYH9PKjwF2auV/AxwxvyZKkiRJm45Juoj8QlWdDpze\nhi8E9p2mzk+AJ3ZomyRJkrTJ8UmOkiRJUkcGbEmSJKkjA7YkSZLUkQFbkiRJ6siALUmSJHVkwJYk\nSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerIgC1JkiR1ZMCWJEmS\nOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuSJEnqyIAtSZIkdWTAliRJkjoy\nYEuSJEkdGbAlSZKkjgzYkiRJUkcGbEmSJKkjA7YkSZLUkQFbkiRJ6siALUmSJHVkwJYkSZI6MmBL\nkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEBW5IkSerIgC1JkiR1ZMCWJEmSOjJgS5Ik\nSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuSJEnqyIAtSZIkdTRrwE6ybZIvJ/lakm8k\neVUr3yvJmUnWJPlAkq1b+TZtfE2bvmxhN0GSJElaPCY5g30jsH9V3R/YBzgwyX7AkcBRVXVP4Brg\n8Fb/cOCaVn5UqydJkiRtEWYN2DW4vo3evr0K2B84oZWvBA5pwwe3cdr0A5KkW4slSZKkRWyiPthJ\ntkpyNnAFcDJwAXBtVd3cqlwK7NaGdwMuAWjTrwN2mmaZK5KsTrJ63bp189sKSZIkaZGYKGBX1c+q\nah9gd2Bf4D7zXXFVHV1Vy6tq+dKlS+e7OEmSJGlRmNNdRKrqWuA04EHA9kmWtEm7A2vb8FpgD4A2\n/S7AVV1aK0mSJC1yk9xFZGmS7dvwHYBHAucxBO0ntGqHASe24VVtnDb91Kqqno2WJEmSFqsls1dh\nV2Blkq0YAvnxVXVSkm8C70/yauCrwDGt/jHAu5OsAa4GDl2AdkuSJEmL0qwBu6rOAR4wTfmFDP2x\nx8t/AjyxS+skSZKkTYxPcpQkSZI6MmBLkiRJHRmwJUmSpI4M2JIkSVJHBmxJkiSpIwO2JEmS1JEB\nW5IkSerIgC1JkiR1ZMCWJEmSOjJgS5IkSR0ZsCVJkqSODNiSJElSRwZsSZIkqSMDtiRJktSRAVuS\nJEnqyIAtSZIkdWTAliRJkjoyYEuSJEkdGbAlSZKkjgzYkiTp/7d3v7GS3WUdwL+PLIUXgAW6KU1b\nKAmLoSKhuGKRIMRCUiChGEmFCCyk0heAgYAmVUw0+qZAQCUisQKhJQrUilDTklLXGtSwTRuBYttA\n18qfLaWtCFXS+Ad9fDGneinb3pnu7849u/fzSW7mnDO/uee5T2bmfufMb84AAwnYAAAwkIANAAAD\nCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnY\nAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAA\nMJCADQAAAwnYAAAwkIANAAADbRqwq+rUqrqmqm6qqhur6k3T9sdU1dVVdct0+ehpe1XVe6rqYFXd\nUFXP2Oo/AgAA5mKZI9jfS/LW7j49yZlJ3lBVpye5IMn+7t6TZP+0niQvTLJn+jk/yfuGVw0AADO1\nacDu7tu7+++n5X9LcnOSk5Ock+TiadjFSV46LZ+T5JJeOJDk+Ko6aXjlAAAwQyvNwa6q05KckeTa\nJCd29+3TVd9McuK0fHKSr2+42aFp231/1/lVdX1VXX/XXXetWDYAAMzT0gG7qh6R5M+SvLm7/3Xj\ndd3dSXqVHXf3Rd29t7v37t69e5WbAgDAbC0VsKvqoVmE6z/u7o9Pm++4d+rHdHnntP22JKduuPkp\n0zYAADjmLXMWkUrygSQ3d/e7N1x1eZJ90/K+JJ/csP3V09lEzkxy94apJAAAcEzbtcSYZyd5VZIv\nVtXnp22/luTCJJdW1XlJvprk3Om6K5O8KMnBJPckee3QigEAYMY2Ddjd/bdJ6n6uPusw4zvJG46w\nLgAAOCr5JkcAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjA\nBgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYA\ngIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICB\nBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRs\nAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGGjTgF1VH6yqO6vqHzZse0xVXV1Vt0yXj562V1W9p6oO\nVtUNVfWMrSweAADmZpkj2B9KcvZ9tl2QZH9370myf1pPkhcm2TP9nJ/kfWPKBACAo8OmAbu7P5Pk\nX+6z+ZwkF0/LFyd56Ybtl/TCgSTHV9VJo4oFAIC5e7BzsE/s7tun5W8mOXFaPjnJ1zeMOzRt+wFV\ndX5VXV9V1991110PsgwAAJiXI/6QY3d3kn4Qt7uou/d2997du3cfaRkAADALDzZg33Hv1I/p8s5p\n+21JTt0w7pRpGwAA7AgPNmBfnmTftLwvySc3bH/1dDaRM5PcvWEqCQAAHPN2bTagqj6S5HlJTqiq\nQ0l+I8mFSS6tqvOSfDXJudPwK5O8KMnBJPckee0W1AwAALO1acDu7lfcz1VnHWZsJ3nDkRYFAABH\nK9/kCAAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQ\ngA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAPt2u4CAHaa0y64YuXbfOXCF29B\nJQBsBUewAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsA\nAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAG\n2rXdBQDASKddcMVK479y4Yu3qJIHb9W/IZnn3wE7lSPYAAAwkIANAAADCdgAADCQOdhsGXMIAYCd\nSMAGALaEAy3sVAI2AMADOBbOTMN6mYMNAAADbckR7Ko6O8nvJXlIkvd394VbsR9geY7AAOxs/g+s\nz/CAXVUPSfLeJC9IcijJdVV1eXffNHpfxzIPgvkwh3BePDYAmLutOIL9zCQHu/vWJKmqjyY5J8ks\nA7bwBACwPY7VHFbdPfYXVr0sydnd/YvT+quS/GR3v/E+485Pcv60+iNJvjS0kHk7Ick/b3cRRxk9\nW41+rU7PVqNfq9Oz1enZavRrdav27AndvXuzQdt2FpHuvijJRdu1/+1UVdd3997truNoomer0a/V\n6dlq9Gt1erY6PVuNfq1uq3q2FWcRuS3JqRvWT5m2AQDAMW8rAvZ1SfZU1ROr6rgkL09y+RbsBwAA\nZmf4FJHu/l5VvTHJVVmcpu+D3X3j6P0c5Xbk1JgjpGer0a/V6dlq9Gt1erY6PVuNfq1uS3o2/EOO\nAACwk/kmRwAAGEjABgCAgQTsNaiqx1TV1VV1y3T56PsZ946qurGqbq6q91RVrbvWuVihZ4+vqk9P\nPbupqk5bb6XzsGy/prGPqqpDVfX766xxbpbpWVU9vao+Oz0ub6iqn9+OWrdTVZ1dVV+qqoNVdcFh\nrn9YVX1suv7anfoY3GiJnr1ler66oar2V9UTtqPOudisXxvG/VxVdVXt+NPQLdOzqjp3up/dWFV/\nsu4a52SJx+Tjq+qaqvrc9Lh80ZHuU8BejwuS7O/uPUn2T+vfp6p+KsmzkzwtyVOT/ESS566zyJnZ\ntGeTS5K8s7ufksW3iN65pvrmZtl+JclvJ/nMWqqat2V6dk+SV3f3jyY5O8nvVtXxa6xxW1XVQ5K8\nN8kLk5ye5BVVdfp9hp2X5Nvd/aQkv5Pk7eutcl6W7Nnnkuzt7qcluSzJO9Zb5Xws2a9U1SOTvCnJ\nteutcH6W6VlV7Unyq0mePT1/vXnthc7EkvexX09yaXefkcXZ7/7gSPcrYK/HOUkunpYvTvLSw4zp\nJA9PclyShyV5aJI71lLdPG3as+kBsqu7r06S7v5ud9+zvhJnZZn7WKrqx5OcmOTTa6przjbtWXd/\nubtvmZa/kcULuE2/wesY8swkB7v71u7+zyQfzaJvG23s42VJztrJ775liZ519zUbnqsOZPF9ETvV\nMvexZHFg4O1J/n2dxc3UMj17XZL3dve3k6S7d+rBp2S5fnWSR03LP5zkG0e6UwF7PU7s7tun5W9m\nEXC+T3d/Nsk1SW6ffq7q7pvXV+LsbNqzJE9O8p2q+vj0ts47p1eqO9Gm/aqqH0ryriS/vM7CZmyZ\n+9j/qapnZvEC+B+3urAZOTnJ1zesH5q2HXZMd38vyd1JHruW6uZpmZ5tdF6ST21pRfO2ab+q6hlJ\nTu3uK9ZZ2Iwtcx97cpInV9XfVdWBqjp7bdXNzzL9+s0kr6yqQ0muTPJLR7rTbfuq9GNNVf1lkscd\n5qq3bVzp7q6qHzg3YlU9KclT8v9HMq6uqud0998ML3YmjrRnWdx/n5PkjCRfS/KxJK9J8oGxlc7D\ngH69PsmV3X1opxxgHNCze3/PSUk+nGRfd//P2CrZqarqlUn2ZmdPB3xA04GBd2fx3M7ydiXZk+R5\nWeSKz1TVj3X3d7a1qvl6RZIPdfe7qupZST5cVU89kud7AXuQ7n7+/V1XVXdU1Undffv0j/pwb9X8\nbJID3f3d6TafSvKsJI0vl+oAAAJRSURBVMdswB7Qs0NJPt/dt063+USSM3OMBuwB/XpWkudU1euT\nPCLJcVX13e5+oPnaR7UBPUtVPSrJFUne1t0HtqjUubotyakb1k+Zth1uzKGq2pXF26vfWk95s7RM\nz1JVz8/ihd5zu/s/1lTbHG3Wr0dm8bmkv54ODDwuyeVV9ZLuvn5tVc7LMvexQ0mu7e7/SvJPVfXl\nLAL3despcVaW6dd5WXzOJt392ap6eJITcgSf6zJFZD0uT7JvWt6X5JOHGfO1JM+tql1V9dAsjmjs\n5Ckiy/TsuiTHV9W9c2J/JslNa6htjjbtV3f/Qnc/vrtPy2KayCXHcrhewqY9q6rjkvx5Fr26bI21\nzcV1SfZU1ROnXrw8i75ttLGPL0vyV72zv8Fs055V1RlJ/jDJS3b43Nhkk351993dfUJ3nzY9dx3I\nom87NVwnyz0uP5HF0etU1QlZTBm5dZ1Fzsgy/fpakrOSpKqeksVn4u46kp0K2OtxYZIXVNUtSZ4/\nraeq9lbV+6cxl2Uxt/OLSb6Q5Avd/RfbUexMbNqz7v7vLILi/qr6YpJK8kfbVO92W+Y+xvdbpmfn\nJvnpJK+pqs9PP0/fnnLXb5pT/cYkV2Xxgv/S7r6xqn6rql4yDftAksdW1cEkb8kDn8HmmLdkz96Z\nxbtIfzrdp+77z37HWLJfbLBkz65K8q2quimLz3f9SnfvyHeWluzXW5O8rqq+kOQjSV5zpAcKfFU6\nAAAM5Ag2AAAMJGADAMBAAjYAAAwkYAMAwEACNgAADCRgAwDAQAI2AAAM9L+u5KXcVBRITgAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RIQWDq5wFb8L"
},
"source": [
"## Ideas:\n",
"\n",
"1. Use `nltk.sentiment.util.mark_negation` to mark negation and flip the scores for words that have the NEG flag. [example](https://kite.com/python/docs/nltk.sentiment.util.mark_negation)\n",
"2. Use bigrams to mark negation (first word has very high Negative score).\n",
"3. Word Embeddings (pretrained [word2vec](https://code.google.com/archive/p/word2vec/) maybe) to find symantically similar words to enrich the datasets.\n",
"4. tf idf for normalizing counts across positive and negative folders."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "P_B8OaORPzfB"
},
"source": [
"## Experiment 4: Using Emolex (with negation) to enrich data\n",
"\n",
"**Hypothesis**: The sentiment of a sentence can be determined by the number of times the word occurs with that rating **in the EmoLex dataset**.\n",
"\n",
"**Approach**:\n",
"Basic approach is as follows:\n",
"1. Convert emotex to a dict with positive, negative and neutral labels.\n",
"1. For each sentence, compute the 'average rating' by looking up the average rate from the dict.\n",
"\n",
"**Observations**:\n",
"1. Precision = **0.651**\n",
"1. Recall = **0.651**\n",
"1. F_Measure = **0.65**\n",
"\n",
"**Conclusion**: Same problem as before. most words are classified into the 'neutral' category. We lose info from that."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7fbSGG-cPzfP"
},
"source": [
"### Converting EmoTex"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "xpy9Gu08PzfT",
"colab": {}
},
"source": [
"neg_emo_dict = pd.melt(neg_emoDF, id_vars=['word'], value_vars=['negative', 'positive', 'neutral']).query('value != 0').drop('value', axis=1)\n",
"\n",
"neg_emo_dict.loc[neg_emo_dict['sense'] == 'negative', 'label'] = -1\n",
"neg_emo_dict.loc[neg_emo_dict['sense'] == 'positive', 'label'] = 1\n",
"neg_emo_dict.loc[neg_emo_dict['sense'] == 'neutral', 'label'] = 0\n",
"neg_emo_dict = defaultdict(float, dict(zip(neg_emo_dict['word'], neg_emo_dict['label'])))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Q_-HAWiOPzfk"
},
"source": [
"### Calculating metrics"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "xH0j0ujVPzfm",
"outputId": "593d4ed6-eba3-4ac8-a605-a9e6ebd9082e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"def test_n_fold_4(testingDF, num_folds = 10):\n",
" cum_model_precision = float()\n",
" cum_model_recall = float()\n",
" cum_model_f = float()\n",
"\n",
" featureDF = testingDF.sample(frac=1)\n",
"\n",
" results = test_df(lookup_sentiment(featureDF, neg_emo_dict))\n",
"\n",
" cum_model_precision = round(results[0][0], 3)\n",
" cum_model_recall = round(results[0][1], 3)\n",
" cum_model_f = round(results[0][2], 3)\n",
" print(('final precision', 'final recall', 'final f_measure'), ' = ', (cum_model_precision, cum_model_recall, cum_model_f))\n",
"\n",
"\n",
"\n",
"for key, value in (zip([\"vanilla\", \"with negation & no stopwords\",\"no stopwords\"], [movieDF, movieDF2, movieDF3])):\n",
" print(key)\n",
"# print(\"vanilla\")\n",
" test_n_fold_4(value)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"vanilla\n",
"('final precision', 'final recall', 'final f_measure') = (0.647, 0.648, 0.647)\n",
"with negation & no stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.419, 0.419, 0.419)\n",
"no stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.651, 0.651, 0.65)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "sqlfH8NuPzfz"
},
"source": [
"### Explaining results\n",
"We have taken care of the problem of having bad data for a lot of dict entries but we are still losing a lot of info as most words don't contribute to calculating the sentiment."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "SbglxCR3Pzf0",
"outputId": "9ab3af26-ab97-43f1-ef09-4a799eaaa3c4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# no more bad learnings :)\n",
"neg_emo_dict['disgust'], neg_emo_dict['disgust_NEG'], neg_emo_dict['love'], neg_emo_dict['love_NEG']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(-1.0, 1.0, 1.0, -1.0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "NKxGAFB4Pzf9",
"outputId": "e9da261f-665f-4419-c604-217796e16587",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 733
}
},
"source": [
"get_random = lambda obj: obj.loc[np.random.choice(obj.index, 1, True),:]\n",
"reviews = featureDF.groupby('label').apply(get_random)['review'].tolist()\n",
"# reviews = featureDF.groupby('label').head(1).sort_values('label')['review'].tolist()\n",
"\n",
"fig, ax = plt.subplots(len(reviews),1, figsize=(10,10), constrained_layout=True)\n",
"\n",
"for i in range(0, len(reviews)):\n",
" review = reviews[i]\n",
" ax[i].hist([neg_emo_dict[word] for word in review], bins=50)\n",
" ax[i].set_title(str(\"label = \" + str(2*i - 1)) + \", len = \" + str(len(review)))\n",
"\n",
"fig.suptitle('Sentiment of each word in ' + str(len(reviews)) + ' sample labels', fontsize=16)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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Sc4EDq+p/j+2q+qckVzHcbu/3GfLB1QzdWqbq+vIjhrvX/CFDl5WfAJcCz6uqk2ezTdJS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\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ed0eFJj4GlDJ",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9RBuozsuLArG",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "z-wB6q_KZxa6"
},
"source": [
"## Experiment 5: Using SentiWordNet (with negation) to enrich data\n",
"\n",
"**Hypothesis**: The sentiment of a sentence can be determined by the number of times the word occurs with that rating **in the EmoLex dataset**.\n",
"\n",
"**Approach**:\n",
"Basic approach is as follows:\n",
"1. Convert emotex to a dict with positive, negative and neutral labels.\n",
"1. For each sentence, compute the 'average rating' by looking up the average rate from the dict.\n",
"\n",
"**Observations**:\n",
"1. Precision = **0.433**\n",
"1. Recall = **0.433**\n",
"1. F_Measure = **0.433**\n",
"\n",
"**Conclusion**: Same problem as before. most words are classified into the 'neutral' category. We lose info from that."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JMjP5lunZxa8"
},
"source": [
"### Converting SentiWordNet"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "328lt6FhZxbA",
"colab": {}
},
"source": [
"neg_sen_dict = defaultdict(float, dict(zip(neg_sen_wordDF['SynsetTerm'], neg_sen_wordDF['Score'])))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "GoCKf-peZxbQ"
},
"source": [
"### Calculating metrics"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "vnrjvbcIZxbU",
"outputId": "ec0ce4db-eefa-4602-b326-682c3185d798",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 128
}
},
"source": [
"cum_model_precision = float()\n",
"cum_model_recall = float()\n",
"cum_model_f = float()\n",
"\n",
"featureDF = movieDF2.sample(frac=1)\n",
"\n",
"results = test_df(lookup_sentiment(featureDF, neg_sen_dict))\n",
"\n",
"cum_model_precision = round(results[0][0], 3)\n",
"cum_model_recall = round(results[0][1], 3)\n",
"cum_model_f = round(results[0][2], 3)\n",
"\n",
"print(('final precision', 'final recall', 'final f_measure'), ' = ', (cum_model_precision, cum_model_recall, cum_model_f))\n",
"results[1]"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"('final precision', 'final recall', 'final f_measure') = (0.433, 0.433, 0.433)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>predicted_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1</td>\n",
" <td>0.126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>-0.142</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label predicted_label\n",
"0 -1 0.126\n",
"1 1 -0.142"
]
},
"metadata": {
"tags": []
},
"execution_count": 43
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RG9fSQjyZxbZ"
},
"source": [
"### Explaining results\n",
"We have taken care of the problem of having bad data for a lot of dict entries but we are still losing a lot of info as most words don't contribute to calculating the sentiment."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "XzgQhOjxZxba",
"outputId": "c268f7a2-530c-4236-99bc-f38244b7205d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# no more bad learnings :)\n",
"neg_sen_dict['disgust'], neg_sen_dict['disgust_NEG'], neg_sen_dict['love'], neg_sen_dict['love_NEG']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(-0.125, 0.125, 0.25, -0.25)"
]
},
"metadata": {
"tags": []
},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "_QKMnxf1Zxbe",
"outputId": "19dfa17c-b6e9-4a56-ef53-683a294e2530",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 733
}
},
"source": [
"get_random = lambda obj: obj.loc[np.random.choice(obj.index, 1, True),:]\n",
"reviews = featureDF.groupby('label').apply(get_random)['review'].tolist()\n",
"# reviews = featureDF.groupby('label').head(1).sort_values('label')['review'].tolist()\n",
"\n",
"fig, ax = plt.subplots(len(reviews),1, figsize=(10,10), constrained_layout=True)\n",
"\n",
"for i in range(0, len(reviews)):\n",
" review = reviews[i]\n",
" ax[i].hist([neg_sen_dict[word] for word in review], bins=50)\n",
" ax[i].set_title(str(\"label = \" + str(2*i - 1)) + \", len = \" + str(len(review)))\n",
"\n",
"fig.suptitle('Sentiment of each word in ' + str(len(reviews)) + ' sample labels', fontsize=16)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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wd1X9at+uqg8kuYLucnt/RZcPVtN1a5mq68vP6a5e8zd0XVZ+CVwMvLCqTpzJOkkbmtz1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\nAAADCdgAADDQ/wFvPJH2MAsqbwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4pzjKTcSgDjG",
"colab_type": "code",
"colab": {}
},
"source": [
"# model specific imports\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"import sklearn.naive_bayes \n",
"from sklearn.metrics import accuracy_score\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fIdxl2R4h0a8"
},
"source": [
"## Experiment 6: Naive Approach + tfidf\n",
"\n",
"**Hypothesis**: The sentiment of a sentence can be determined by the number of times the word occurs with that rating **in the dataset**.\n",
"\n",
"**Approach**:\n",
"Basic approach is as follows:\n",
"1. Normalize Data using tf -df\n",
"1. Filter Data.\n",
"1. count the number of times a word occurs with that rating.\n",
"1. take the mean of this measure across all ratings for each word.\n",
"1. Put this in a dict.\n",
"1. For each sentence, compute the 'average rating' by looking up the average rate for each word from the dict, summing it up and dividing by length of sentence.\n",
"\n",
"**Observations**:\n",
"1. Precision = **0.623**\n",
"1. Recall = **0.623**\n",
"1. F_Measure = **0.623**\n",
"\n",
"**Conclusion**: This sucks."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "ae_LuGXVh0a9",
"outputId": "a7b29501-f8ea-4d81-8f43-1840b37f11c8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
}
},
"source": [
"def cutoff_excess(movieDF):\n",
" min_class = int(movieDF.groupby('label')['category'].count().min())\n",
" naive_normDF = movieDF.groupby('label').head(min_class)\n",
" cutoffDF = movieDF[~movieDF.isin(naive_normDF)].dropna()\n",
" return naive_normDF, cutoffDF\n",
"\n",
"naive_normDF, cutoffDF = cutoff_excess(movieDF)\n",
"naive_normDF['label'].hist(), cutoffDF['label'].hist(bins=2)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(<matplotlib.axes._subplots.AxesSubplot at 0x7f10eff95a58>,\n",
" <matplotlib.axes._subplots.AxesSubplot at 0x7f10eff95a58>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 47
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVer/ASK/tGh8l0uGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iw8trQXEikBo",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-OjcBRGGt9N8",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "WG3qr33lh0bA"
},
"source": [
"### Count occurances\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "1Pgp5YK5h0bB",
"outputId": "11c7f7f9-e6d4-415f-ae9e-09c59cb6ad4a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"def get_word_counts_tfidf(normDF):\n",
" normalizer = TfidfVectorizer(input=\"content\", strip_accents='ascii', stop_words='english').fit(normDF['review'].apply(lambda row : \" \".join(row)).values )\n",
" tf_idf_dict = defaultdict(lambda: max(normalizer.idf_), [(w, normalizer.idf_[i]) for w, i in normalizer.vocabulary_.items()])\n",
" countDF = (normDF['review']\n",
" .apply(pd.Series)\n",
" .merge(normDF, left_index = True, right_index = True)\n",
" .drop('review', axis=1)\n",
" .melt(id_vars = ['label', 'category'], value_name = \"review_word\")\n",
" )\n",
" countDF['review_word'] = countDF['review_word'].apply(lambda word : lemmatizer.lemmatize(str(word)))\n",
" countDF['idf'] = countDF['review_word'].map(tf_idf_dict)\n",
" countDF['sent'] = countDF['review_word'].map(sen_dict)\n",
" return countDF\n",
"countDF = get_word_counts_tfidf(movieDF)\n",
"countDF.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>category</th>\n",
" <th>variable</th>\n",
" <th>review_word</th>\n",
" <th>idf</th>\n",
" <th>sent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>plot</td>\n",
" <td>1.806816</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>so</td>\n",
" <td>7.908255</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>synopsis</td>\n",
" <td>4.396710</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>the</td>\n",
" <td>7.908255</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1</td>\n",
" <td>neg</td>\n",
" <td>0</td>\n",
" <td>call</td>\n",
" <td>7.908255</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label category variable review_word idf sent\n",
"0 -1 neg 0 plot 1.806816 0.0\n",
"1 -1 neg 0 so 7.908255 0.0\n",
"2 -1 neg 0 synopsis 4.396710 0.0\n",
"3 -1 neg 0 the 7.908255 0.0\n",
"4 -1 neg 0 call 7.908255 0.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rbb57fHmwYVX",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "sD8yWS2Ph0bC"
},
"source": [
"### Generate dict"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "mBI9lZ2wh0bC",
"outputId": "5eb1ee50-f45a-4a17-b31c-6f8afd989720",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"def get_dict_from_counts_tfidf(countDF):\n",
" test = (countDF.groupby(['label', 'review_word']).mean().reset_index())\n",
" test['tot'] = round(test['idf'] * test['sent'], 3)\n",
" test = test.groupby(['review_word'])['tot'].mean().sort_values(ascending=False).reset_index()\n",
" sen_words = defaultdict(float, dict(zip(test['review_word'], test['tot'])))\n",
" return sen_words\n",
"\n",
"sentiment_dict = get_dict_from_counts_tfidf(countDF)\n",
"\n",
"sentiment_dict['bad'], sentiment_dict['worst'], sentiment_dict['okay'], sentiment_dict['good'], sentiment_dict['amazing']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(-1.462, 0.391, 0.0, 0.953, 2.713)"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "pKB8kEnmh0bK"
},
"source": [
"### Calculating metrics"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "aYWejgERh0bL",
"outputId": "d76ad055-fa0e-412d-8711-4d546e3802e4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"def test_n_fold_5(testingDF, num_folds = 10):\n",
" featureDF = testingDF.sample(frac=1)\n",
"\n",
" cum_model_precision = float()\n",
" cum_model_recall = float()\n",
" cum_model_f = float()\n",
"\n",
" k_fold_gen = KFold(n_splits=num_folds)\n",
" for train_idx, test_idx in k_fold_gen.split(featureDF):\n",
"\n",
" train = featureDF.iloc[train_idx]\n",
" test = featureDF.iloc[test_idx]\n",
" words_dict = get_dict_from_counts_tfidf(get_word_counts_tfidf(cutoff_excess(train)[0]))\n",
" enrichedDF = lookup_sentiment(test, words_dict)\n",
" model_precision, model_recall, model_f = test_df(enrichedDF)[0]\n",
" cum_model_precision += model_precision\n",
" cum_model_recall += model_recall\n",
" cum_model_f += model_f\n",
"\n",
" cum_model_precision = round(cum_model_precision / num_folds, 3)\n",
" cum_model_recall = round(cum_model_recall / num_folds, 3)\n",
" cum_model_f = round(cum_model_f / num_folds, 3)\n",
"\n",
" print(('final precision', 'final recall', 'final f_measure'), ' = ', (cum_model_precision, cum_model_recall, cum_model_f))\n",
"\n",
"for key, value in (zip([\"vanilla\", \"with negation & with stopwords\",\"with stopwords\"], [movieDF, movieDF2, movieDF3])):\n",
" print(key)\n",
" test_n_fold_5(value, num_folds = 5)\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"vanilla\n",
"('final precision', 'final recall', 'final f_measure') = (0.576, 0.576, 0.576)\n",
"with negation & with stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.554, 0.556, 0.551)\n",
"with stopwords\n",
"('final precision', 'final recall', 'final f_measure') = (0.623, 0.623, 0.623)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "E7GFvbsPh0bM"
},
"source": [
"### Explaining performance\n",
"\n",
"Most words fall under the neutral category. Hence dont effect perf. Additionally, the dict has incorrect values for a lot of words. Also there is a lot of noise in the dict. "
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "OrW18Rudh0bN",
"outputId": "6a57d471-cbc4-49ad-a6e6-7a285d56115a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173
}
},
"source": [
"testDF.groupby('label').head(2).sort_values('label')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category</th>\n",
" <th>review</th>\n",
" <th>label</th>\n",
" <th>sentiment</th>\n",
" <th>predicted_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>neg</td>\n",
" <td>[plot, two, teen, couples, go, to, a, church, ...</td>\n",
" <td>-1</td>\n",
" <td>360.422176</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neg</td>\n",
" <td>[so, ask, yourself, what, 8mm, eight, millimet...</td>\n",
" <td>-1</td>\n",
" <td>358.972469</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>pos</td>\n",
" <td>[films, adapted, from, comic, books, have, had...</td>\n",
" <td>1</td>\n",
" <td>393.895105</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1001</th>\n",
" <td>pos</td>\n",
" <td>[every, now, and, then, a, movie, comes, along...</td>\n",
" <td>1</td>\n",
" <td>411.541667</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category review label \\\n",
"0 neg [plot, two, teen, couples, go, to, a, church, ... -1 \n",
"1 neg [so, ask, yourself, what, 8mm, eight, millimet... -1 \n",
"1000 pos [films, adapted, from, comic, books, have, had... 1 \n",
"1001 pos [every, now, and, then, a, movie, comes, along... 1 \n",
"\n",
" sentiment predicted_label \n",
"0 360.422176 -1 \n",
"1 358.972469 -1 \n",
"1000 393.895105 -1 \n",
"1001 411.541667 1 "
]
},
"metadata": {
"tags": []
},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Bbl2VVLIh0bO",
"outputId": "a295f6da-86a1-44ad-f05d-5ffe6b4f8f85",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"sentiment_dict['disgust'], sentiment_dict['shit'], sentiment_dict['okay'], sentiment_dict['like']"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(-0.765, -1.336, 0.0, 0.161)"
]
},
"metadata": {
"tags": []
},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "aZaEB3hlh0bP",
"outputId": "8f02e609-f6a7-45d5-80fd-79ca4ba4f207",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 733
}
},
"source": [
"get_random = lambda obj: obj.loc[np.random.choice(obj.index, 1, True),:]\n",
"reviews = testDF.groupby('label').apply(get_random)['review'].tolist()\n",
"# reviews = featureDF.groupby('label').head(1).sort_values('label')['review'].tolist()\n",
"\n",
"fig, ax = plt.subplots(len(reviews),1, figsize=(10,10), constrained_layout=True)\n",
"\n",
"props = dict(boxstyle='square', facecolor='white', alpha=0.5)\n",
"\n",
"for i in range(0, len(reviews)):\n",
" review = reviews[i]\n",
" ax[i].hist([sentiment_dict[word] for word in review], bins=50)\n",
" ax[i].set_title(str(\"label = \" + str(2*i - 1)) + \", len = \" + str(len(review)))\n",
"\n",
"\n",
"\n",
"fig.suptitle('Histogram of sentiment lookup for each word in ' + str(len(reviews)) + ' classes', fontsize=16)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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Yr1MB+5cYhrz8InwmeSZ3Hs7wBWDPJI9bz/ZM4ksMQ9NezTD+uarqGoYPWm9leN5He7A3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\nBvpfCMjB7LOhfq0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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