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Tweet Sentiment Extraction NLP.ipynb
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| { | |
| "metadata": { | |
| "kernelspec": { | |
| "language": "python", | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "name": "python", | |
| "version": "3.10.12", | |
| "mimetype": "text/x-python", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "pygments_lexer": "ipython3", | |
| "nbconvert_exporter": "python", | |
| "file_extension": ".py" | |
| }, | |
| "kaggle": { | |
| "accelerator": "none", | |
| "dataSources": [ | |
| { | |
| "sourceId": 16295, | |
| "databundleVersionId": 1099992, | |
| "sourceType": "competition" | |
| } | |
| ], | |
| "dockerImageVersionId": 30615, | |
| "isInternetEnabled": true, | |
| "language": "python", | |
| "sourceType": "notebook", | |
| "isGpuEnabled": false | |
| }, | |
| "colab": { | |
| "provenance": [], | |
| "include_colab_link": true | |
| } | |
| }, | |
| "nbformat_minor": 0, | |
| "nbformat": 4, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/ZhiyaoShu/bba0a711f2f4b8f368c453dc9ae07641/tweet_sentiment_extraction_nlp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install -q kaggle" | |
| ], | |
| "metadata": { | |
| "id": "tEsX1rtj0svh" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! cp /content/drive/MyDrive/kaggle.json ~/.kaggle/" | |
| ], | |
| "metadata": { | |
| "id": "IEX4Y7n81pbZ" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! chmod 600 ~/.kaggle/kaggle.json" | |
| ], | |
| "metadata": { | |
| "id": "qeVgGSVS3iDU" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! kaggle datasets list" | |
| ], | |
| "metadata": { | |
| "id": "v_K6hdnu3jwB" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "import os\n", | |
| "for dirname, _, filenames in os.walk('/kaggle/input'):\n", | |
| " for filename in filenames:\n", | |
| " print(os.path.join(dirname, filename))\n" | |
| ], | |
| "metadata": { | |
| "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", | |
| "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:03:55.911081Z", | |
| "iopub.execute_input": "2023-12-09T03:03:55.912258Z", | |
| "iopub.status.idle": "2023-12-09T03:03:56.443447Z", | |
| "shell.execute_reply.started": "2023-12-09T03:03:55.912211Z", | |
| "shell.execute_reply": "2023-12-09T03:03:56.441627Z" | |
| }, | |
| "trusted": true, | |
| "id": "BCLtIvzkmh6U" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "drive.mount('/content/drive')" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "dMnUsKzuHHgq", | |
| "outputId": "9b1f2c09-f65d-4f84-b802-0b4938a88ef4" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Mounted at /content/drive\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Intro: This is a practice of social media sentiment extraction" | |
| ], | |
| "metadata": { | |
| "id": "dukkVz6omh6X" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "print(tf.__version__)" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:05:31.877064Z", | |
| "iopub.execute_input": "2023-12-09T03:05:31.877820Z", | |
| "iopub.status.idle": "2023-12-09T03:05:51.675825Z", | |
| "shell.execute_reply.started": "2023-12-09T03:05:31.877784Z", | |
| "shell.execute_reply": "2023-12-09T03:05:51.674840Z" | |
| }, | |
| "trusted": true, | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "wmxvLE-ymh6Y", | |
| "outputId": "08cc362d-d7eb-466c-d359-982e77055a2f" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "2.15.0\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! kaggle competitions download -c tweet-sentiment-extraction" | |
| ], | |
| "metadata": { | |
| "id": "gPNHxpMH3rXS", | |
| "outputId": "23771922-368c-4770-fb45-c3149f4af9fd", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Downloading tweet-sentiment-extraction.zip to /content\n", | |
| "\r 0% 0.00/1.39M [00:00<?, ?B/s]\n", | |
| "\r100% 1.39M/1.39M [00:00<00:00, 133MB/s]\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! unzip tweet-sentiment-extraction.zip" | |
| ], | |
| "metadata": { | |
| "id": "ENI-yrZi4CUt", | |
| "outputId": "d0c64924-1e65-4749-e858-e898fa75ec7b", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Archive: tweet-sentiment-extraction.zip\n", | |
| " inflating: sample_submission.csv \n", | |
| " inflating: test.csv \n", | |
| " inflating: train.csv \n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "test_data = pd.read_csv(\"/content/test.csv\")\n", | |
| "train_data = pd.read_csv(\"/content/train.csv\")\n", | |
| "\n", | |
| "test_data.head()\n", | |
| "train_data.head()" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:05:51.887897Z", | |
| "iopub.execute_input": "2023-12-09T03:05:51.888351Z", | |
| "iopub.status.idle": "2023-12-09T03:05:52.026869Z", | |
| "shell.execute_reply.started": "2023-12-09T03:05:51.888316Z", | |
| "shell.execute_reply": "2023-12-09T03:05:52.025505Z" | |
| }, | |
| "trusted": true, | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 258 | |
| }, | |
| "id": "yfvBvkeGmh6Y", | |
| "outputId": "eb8b2cf9-1e40-4aba-c928-ef449a93cb73" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " textID text \\\n", | |
| "0 cb774db0d1 I`d have responded, if I were going \n", | |
| "1 549e992a42 Sooo SAD I will miss you here in San Diego!!! \n", | |
| "2 088c60f138 my boss is bullying me... \n", | |
| "3 9642c003ef what interview! leave me alone \n", | |
| "4 358bd9e861 Sons of ****, why couldn`t they put them on t... \n", | |
| "\n", | |
| " selected_text sentiment \n", | |
| "0 I`d have responded, if I were going neutral \n", | |
| "1 Sooo SAD negative \n", | |
| "2 bullying me negative \n", | |
| "3 leave me alone negative \n", | |
| "4 Sons of ****, negative " | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-7b4501f3-e383-4523-881d-b8fc1dd19732\" class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
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| " <th>textID</th>\n", | |
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| " <th>sentiment</th>\n", | |
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| " <td>neutral</td>\n", | |
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| " <th>1</th>\n", | |
| " <td>549e992a42</td>\n", | |
| " <td>Sooo SAD I will miss you here in San Diego!!!</td>\n", | |
| " <td>Sooo SAD</td>\n", | |
| " <td>negative</td>\n", | |
| " </tr>\n", | |
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| " <td>my boss is bullying me...</td>\n", | |
| " <td>bullying me</td>\n", | |
| " <td>negative</td>\n", | |
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| " <td>what interview! leave me alone</td>\n", | |
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| " <tr>\n", | |
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| " <td>358bd9e861</td>\n", | |
| " <td>Sons of ****, why couldn`t they put them on t...</td>\n", | |
| " <td>Sons of ****,</td>\n", | |
| " <td>negative</td>\n", | |
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| " height: 32px;\n", | |
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| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
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| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
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| " [theme=dark] .colab-df-convert {\n", | |
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| " const element = document.querySelector('#df-7b4501f3-e383-4523-881d-b8fc1dd19732');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
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| " if (!dataTable) return;\n", | |
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| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
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| " docLink.innerHTML = docLinkHtml;\n", | |
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| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
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| " 20% {\n", | |
| " border-color: transparent;\n", | |
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| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
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| " border-bottom-color: var(--fill-color);\n", | |
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| " 90% {\n", | |
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| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-7f93d536-0713-47f8-a2bd-a22ba0017957 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "train_data", | |
| "summary": "{\n \"name\": \"train_data\",\n \"rows\": 27481,\n \"fields\": [\n {\n \"column\": \"textID\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 27481,\n \"samples\": [\n \"a7f72a928a\",\n \"ef42dee96c\",\n \"07d17131b1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 27480,\n \"samples\": [\n \" Enjoy! Family trumps everything\",\n \" --of them kinda turns me off of it all. And then I buy more of them and dig a deeper hole, etc. ;;\",\n \"Clive it`s my birthday pat me http://apps.facebook.com/dogbook/profile/view/6386106\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"selected_text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 22463,\n \"samples\": [\n \"we win\",\n \"YES!!! haahaaa.! break out the jellybeaniesss!\",\n \"hay wats ur AIM? we should chat\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 23 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Check basic information\n", | |
| "train_data.describe()" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:06:18.849022Z", | |
| "iopub.execute_input": "2023-12-09T03:06:18.849502Z", | |
| "iopub.status.idle": "2023-12-09T03:06:18.943085Z", | |
| "shell.execute_reply.started": "2023-12-09T03:06:18.849465Z", | |
| "shell.execute_reply": "2023-12-09T03:06:18.941595Z" | |
| }, | |
| "trusted": true, | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 175 | |
| }, | |
| "id": "LGQvMS65mh6Y", | |
| "outputId": "e2ef44af-d069-4bb1-99f2-563bc35ec443" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " textID text selected_text \\\n", | |
| "count 27481 27480 27480 \n", | |
| "unique 27481 27480 22463 \n", | |
| "top cb774db0d1 I`d have responded, if I were going good \n", | |
| "freq 1 1 199 \n", | |
| "\n", | |
| " sentiment \n", | |
| "count 27481 \n", | |
| "unique 3 \n", | |
| "top neutral \n", | |
| "freq 11118 " | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-ad0427ca-cb16-43e0-82bb-b17a3e70e007\" class=\"colab-df-container\">\n", | |
| " <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>textID</th>\n", | |
| " <th>text</th>\n", | |
| " <th>selected_text</th>\n", | |
| " <th>sentiment</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>27481</td>\n", | |
| " <td>27480</td>\n", | |
| " <td>27480</td>\n", | |
| " <td>27481</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>unique</th>\n", | |
| " <td>27481</td>\n", | |
| " <td>27480</td>\n", | |
| " <td>22463</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>top</th>\n", | |
| " <td>cb774db0d1</td>\n", | |
| " <td>I`d have responded, if I were going</td>\n", | |
| " <td>good</td>\n", | |
| " <td>neutral</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>freq</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>199</td>\n", | |
| " <td>11118</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad0427ca-cb16-43e0-82bb-b17a3e70e007')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
| " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-ad0427ca-cb16-43e0-82bb-b17a3e70e007 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-ad0427ca-cb16-43e0-82bb-b17a3e70e007');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| "\n", | |
| "<div id=\"df-eb23c673-c67d-4a2b-ab0e-fae860d855eb\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eb23c673-c67d-4a2b-ab0e-fae860d855eb')\"\n", | |
| " title=\"Suggest charts\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
| " </g>\n", | |
| "</svg>\n", | |
| " </button>\n", | |
| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-eb23c673-c67d-4a2b-ab0e-fae860d855eb button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "summary": "{\n \"name\": \"train_data\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"textID\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"27481\",\n \"cb774db0d1\",\n \"1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"27480\",\n \" I`d have responded, if I were going\",\n \"1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"selected_text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n 22463,\n \"199\",\n \"27480\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n 3,\n \"11118\",\n \"27481\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 24 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "test_data.describe()" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:06:22.059012Z", | |
| "iopub.execute_input": "2023-12-09T03:06:22.059505Z", | |
| "iopub.status.idle": "2023-12-09T03:06:22.087533Z", | |
| "shell.execute_reply.started": "2023-12-09T03:06:22.059474Z", | |
| "shell.execute_reply": "2023-12-09T03:06:22.086136Z" | |
| }, | |
| "trusted": true, | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 175 | |
| }, | |
| "id": "ziO__MDtmh6Z", | |
| "outputId": "d4ed2938-e7de-4902-cf06-810dbb86a567" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " textID text \\\n", | |
| "count 3534 3534 \n", | |
| "unique 3534 3534 \n", | |
| "top f87dea47db Last session of the day http://twitpic.com/67ezh \n", | |
| "freq 1 1 \n", | |
| "\n", | |
| " sentiment \n", | |
| "count 3534 \n", | |
| "unique 3 \n", | |
| "top neutral \n", | |
| "freq 1430 " | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-f2470ee3-a39b-4d22-8486-884b850c3405\" class=\"colab-df-container\">\n", | |
| " <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>textID</th>\n", | |
| " <th>text</th>\n", | |
| " <th>sentiment</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>3534</td>\n", | |
| " <td>3534</td>\n", | |
| " <td>3534</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>unique</th>\n", | |
| " <td>3534</td>\n", | |
| " <td>3534</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>top</th>\n", | |
| " <td>f87dea47db</td>\n", | |
| " <td>Last session of the day http://twitpic.com/67ezh</td>\n", | |
| " <td>neutral</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>freq</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1430</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2470ee3-a39b-4d22-8486-884b850c3405')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
| " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-f2470ee3-a39b-4d22-8486-884b850c3405 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-f2470ee3-a39b-4d22-8486-884b850c3405');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| "\n", | |
| "<div id=\"df-27dac3cc-b7f4-4396-acdb-6ee946af87e7\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-27dac3cc-b7f4-4396-acdb-6ee946af87e7')\"\n", | |
| " title=\"Suggest charts\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
| " </g>\n", | |
| "</svg>\n", | |
| " </button>\n", | |
| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-27dac3cc-b7f4-4396-acdb-6ee946af87e7 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "summary": "{\n \"name\": \"test_data\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"textID\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"3534\",\n \"f87dea47db\",\n \"1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"3534\",\n \"Last session of the day http://twitpic.com/67ezh\",\n \"1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n 3,\n \"1430\",\n \"3534\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 25 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Check for null values of train data\n", | |
| "train_data.isna().sum()" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:06:02.642708Z", | |
| "iopub.execute_input": "2023-12-09T03:06:02.643173Z", | |
| "iopub.status.idle": "2023-12-09T03:06:02.666775Z", | |
| "shell.execute_reply.started": "2023-12-09T03:06:02.643140Z", | |
| "shell.execute_reply": "2023-12-09T03:06:02.665157Z" | |
| }, | |
| "trusted": true, | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "sW_rLEvTmh6Z", | |
| "outputId": "f5974af5-e699-4372-d4de-32d330e96737" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "textID 0\n", | |
| "text 1\n", | |
| "selected_text 1\n", | |
| "sentiment 0\n", | |
| "dtype: int64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 26 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Drop null values\n", | |
| "train_data.dropna(inplace=True)" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:06:34.309793Z", | |
| "iopub.execute_input": "2023-12-09T03:06:34.310425Z", | |
| "iopub.status.idle": "2023-12-09T03:06:34.336322Z", | |
| "shell.execute_reply.started": "2023-12-09T03:06:34.310375Z", | |
| "shell.execute_reply": "2023-12-09T03:06:34.334742Z" | |
| }, | |
| "trusted": true, | |
| "id": "BYth5ceQmh6Z" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Check for null values of test data\n", | |
| "test_data.isna().sum()" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:06:38.689673Z", | |
| "iopub.execute_input": "2023-12-09T03:06:38.690104Z", | |
| "iopub.status.idle": "2023-12-09T03:06:38.701870Z", | |
| "shell.execute_reply.started": "2023-12-09T03:06:38.690071Z", | |
| "shell.execute_reply": "2023-12-09T03:06:38.700699Z" | |
| }, | |
| "trusted": true, | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Gad-bU3qmh6a", | |
| "outputId": "b91e3e03-8c82-4993-9f35-4c98313a07eb" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "textID 0\n", | |
| "text 0\n", | |
| "sentiment 0\n", | |
| "dtype: int64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 28 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Drop null values\n", | |
| "test_data.dropna(inplace=True)" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:06:50.905069Z", | |
| "iopub.execute_input": "2023-12-09T03:06:50.906112Z", | |
| "iopub.status.idle": "2023-12-09T03:06:50.913867Z", | |
| "shell.execute_reply.started": "2023-12-09T03:06:50.906067Z", | |
| "shell.execute_reply": "2023-12-09T03:06:50.912656Z" | |
| }, | |
| "trusted": true, | |
| "id": "yD8fZWvnmh6a" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Count seniment values\n", | |
| "train_data['sentiment'].value_counts()" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:14:49.588524Z", | |
| "iopub.execute_input": "2023-12-09T03:14:49.589545Z", | |
| "iopub.status.idle": "2023-12-09T03:14:49.604619Z", | |
| "shell.execute_reply.started": "2023-12-09T03:14:49.589491Z", | |
| "shell.execute_reply": "2023-12-09T03:14:49.603145Z" | |
| }, | |
| "trusted": true, | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "tIX5rua1mh6b", | |
| "outputId": "d7611ee3-cd43-4e04-d405-7dbf3a8b6fcf" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "neutral 11117\n", | |
| "positive 8582\n", | |
| "negative 7781\n", | |
| "Name: sentiment, dtype: int64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 30 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Analysis text length\n", | |
| "train_data['text_length'] = train_data['text'].apply(len)\n", | |
| "train_data.groupby('sentiment')['text_length'].mean()" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:06:53.491565Z", | |
| "iopub.execute_input": "2023-12-09T03:06:53.492804Z", | |
| "iopub.status.idle": "2023-12-09T03:06:53.531364Z", | |
| "shell.execute_reply.started": "2023-12-09T03:06:53.492759Z", | |
| "shell.execute_reply": "2023-12-09T03:06:53.530181Z" | |
| }, | |
| "trusted": true, | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "gvJ2x3Rtmh6a", | |
| "outputId": "3aff7c0a-44fe-4ca8-80c3-24d8cfd6867a" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "sentiment\n", | |
| "negative 70.488112\n", | |
| "neutral 65.206800\n", | |
| "positive 70.419133\n", | |
| "Name: text_length, dtype: float64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 31 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Analysis selected text length\n", | |
| "train_data['selected_text_length'] = train_data['selected_text'].apply(len)\n", | |
| "train_data.groupby('sentiment')['selected_text_length'].mean()\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "YMMpNaeUnRag", | |
| "outputId": "a551d4d5-ec7a-42a6-9814-63f4a4b08567" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "sentiment\n", | |
| "negative 19.970698\n", | |
| "neutral 62.765134\n", | |
| "positive 18.124680\n", | |
| "Name: selected_text_length, dtype: float64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 32 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# ANOVA results analysis\n", | |
| "from scipy import stats\n", | |
| "\n", | |
| "f_val, p_val = stats.f_oneway(train_data[train_data['sentiment'] == 'positive']['text_length'],\n", | |
| " train_data[train_data['sentiment'] == 'negative']['text_length'],\n", | |
| " train_data[train_data['sentiment'] == 'neutral']['text_length'])\n", | |
| "\n", | |
| "print(\"ANOVA Test Results:\")\n", | |
| "print(f\"F-statistic: {f_val}\")\n", | |
| "print(f\"P-value: {p_val}\")\n", | |
| "\n", | |
| "# Interpret the results\n", | |
| "alpha = 0.05\n", | |
| "if p_val<alpha:\n", | |
| " print(\"The means of at least two groups are significantly different.\")\n", | |
| "else:\n", | |
| " print(\"There is no significant difference in the means of the groups.\")" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:17:23.632685Z", | |
| "iopub.execute_input": "2023-12-09T03:17:23.633161Z", | |
| "iopub.status.idle": "2023-12-09T03:18:41.566408Z", | |
| "shell.execute_reply.started": "2023-12-09T03:17:23.633128Z", | |
| "shell.execute_reply": "2023-12-09T03:18:41.564637Z" | |
| }, | |
| "trusted": true, | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "8u8vnTYrmh6b", | |
| "outputId": "59af0801-38f3-4f6f-e7d6-3e6ef11982bc" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "ANOVA Test Results:\n", | |
| "F-statistic: 72.2127709711816\n", | |
| "P-value: 5.254438748898152e-32\n", | |
| "The means of at least two groups are significantly different.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Data Processing" | |
| ], | |
| "metadata": { | |
| "id": "j5gAPl2Umh6b" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import re\n", | |
| "import string\n", | |
| "\n", | |
| "def clean_text(text):\n", | |
| " text = text.lower()\n", | |
| " text = re.sub(r\"what's\", \"what is \", text)\n", | |
| " text = re.sub(r\"\\'s\", \" \", text)\n", | |
| " text = re.sub(r\"\\'ve\", \" have \", text)\n", | |
| " text = re.sub(r\"can't\", \"cannot \", text)\n", | |
| " text = re.sub(r\"n't\", \" not \", text)\n", | |
| " return text\n", | |
| "\n", | |
| "train_data['text'] = train_data['text'].apply(clean_text)\n", | |
| "test_data['text'] = test_data['text'].apply(clean_text)" | |
| ], | |
| "metadata": { | |
| "id": "BphYjewqpCaL" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from tensorflow.keras.preprocessing.text import Tokenizer\n", | |
| "\n", | |
| "# Tokenize the text\n", | |
| "token = Tokenizer(num_words = 500)\n", | |
| "token.fit_on_texts(train_data['text'])\n", | |
| "\n", | |
| "# Convert texts to sequence of integers\n", | |
| "train_sequences = token.texts_to_sequences(train_data['text'])\n", | |
| "test_sequences = token.texts_to_sequences(test_data['text'])\n", | |
| "\n", | |
| "# Convert labels to categorical one-hot encoding\n", | |
| "train_labels = pd.get_dummies(train_data['sentiment']).values\n", | |
| "test_labels = pd.get_dummies(test_data['sentiment']).values\n" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-09T03:21:59.215359Z", | |
| "iopub.execute_input": "2023-12-09T03:21:59.215864Z", | |
| "iopub.status.idle": "2023-12-09T03:22:00.003619Z", | |
| "shell.execute_reply.started": "2023-12-09T03:21:59.215829Z", | |
| "shell.execute_reply": "2023-12-09T03:22:00.001594Z" | |
| }, | |
| "trusted": true, | |
| "id": "1iM34W3Kmh6b" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Vectorize the text\n", | |
| "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", | |
| "\n", | |
| "max_len = max([len(x) for x in train_sequences])\n", | |
| "train_padded = pad_sequences(train_sequences, maxlen = max_len, padding = \"post\", truncating = \"post\")\n", | |
| "test_padded = pad_sequences(test_sequences, maxlen = max_len, padding = \"post\", truncating = \"post\")\n", | |
| "\n", | |
| "print(max_len)\n", | |
| "print(train_padded.shape)\n", | |
| "print(test_padded.shape)\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "46S4ueo3mh6b", | |
| "outputId": "7a345779-d3ea-4656-fdd4-65d819a0ffa2" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "32\n", | |
| "(27480, 32)\n", | |
| "(3534, 32)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Build the model" | |
| ], | |
| "metadata": { | |
| "id": "_B7z4_rmsNbJ" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from tensorflow.keras.models import Sequential\n", | |
| "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", | |
| "from tensorflow.keras.layers import Embedding, LSTM, GRU, Dense, Dropout\n", | |
| "from tensorflow.keras.callbacks import EarlyStopping\n", | |
| "from tensorflow.keras.preprocessing.text import Tokenizer\n", | |
| "from tensorflow.keras.utils import to_categorical\n", | |
| "import numpy as np\n", | |
| "import tensorflow as tf" | |
| ], | |
| "metadata": { | |
| "execution": { | |
| "iopub.status.busy": "2023-12-08T08:48:28.404499Z", | |
| "iopub.execute_input": "2023-12-08T08:48:28.404989Z", | |
| "iopub.status.idle": "2023-12-08T08:48:28.756170Z", | |
| "shell.execute_reply.started": "2023-12-08T08:48:28.404953Z", | |
| "shell.execute_reply": "2023-12-08T08:48:28.754753Z" | |
| }, | |
| "trusted": true, | |
| "id": "vzFXiRI5mh6b" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "np.random.seed(42)\n", | |
| "tf.random.set_seed(42)" | |
| ], | |
| "metadata": { | |
| "id": "rmY2dSgAI6wL" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def lstm_model(max_len):\n", | |
| " model = Sequential()\n", | |
| "\n", | |
| " # Add embedding layer\n", | |
| " model.add(Embedding(input_dim = 500, output_dim = 32, input_length = max_len))\n", | |
| "\n", | |
| " # Add LSTM layer\n", | |
| " model.add(LSTM(64, return_sequences=True))\n", | |
| " model.add(Dropout(0.5))\n", | |
| "\n", | |
| " # Add LSTM layer without returning sequences\n", | |
| " model.add(LSTM(32))\n", | |
| " model.add(Dropout(0.5))\n", | |
| "\n", | |
| " # Add dense layer\n", | |
| " model.add(Dense(3, activation='sigmoid'))\n", | |
| "\n", | |
| " return model\n", | |
| "\n", | |
| "def gru_model(max_len):\n", | |
| " model = Sequential()\n", | |
| "\n", | |
| " # Add embedding layer\n", | |
| " model.add(Embedding(input_dim = 500, output_dim = 32, input_length = max_len))\n", | |
| " # Add GRU layer\n", | |
| " model.add(GRU(128, return_sequences=True))\n", | |
| " model.add(Dropout(0.5))\n", | |
| "\n", | |
| " # Add GRU layer without returning sequences\n", | |
| " model.add(GRU(32))\n", | |
| " model.add(Dropout(0.5))\n", | |
| "\n", | |
| " # Add dense layer\n", | |
| " model.add(Dense(3, activation='sigmoid'))\n", | |
| "\n", | |
| " return model" | |
| ], | |
| "metadata": { | |
| "id": "h51X_LSwsSd1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "lstm = lstm_model(max_len)\n", | |
| "lstm.summary()\n", | |
| "# Complie the models\n", | |
| "lstm.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", | |
| "gru_model(max_len).compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Z2YzFGqu0iIz", | |
| "outputId": "c3bd49c0-6aba-418d-aac3-8174818b90ca" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Model: \"sequential\"\n", | |
| "_________________________________________________________________\n", | |
| " Layer (type) Output Shape Param # \n", | |
| "=================================================================\n", | |
| " embedding (Embedding) (None, 32, 32) 16000 \n", | |
| " \n", | |
| " lstm (LSTM) (None, 32, 64) 24832 \n", | |
| " \n", | |
| " dropout (Dropout) (None, 32, 64) 0 \n", | |
| " \n", | |
| " lstm_1 (LSTM) (None, 32) 12416 \n", | |
| " \n", | |
| " dropout_1 (Dropout) (None, 32) 0 \n", | |
| " \n", | |
| " dense (Dense) (None, 3) 99 \n", | |
| " \n", | |
| "=================================================================\n", | |
| "Total params: 53347 (208.39 KB)\n", | |
| "Trainable params: 53347 (208.39 KB)\n", | |
| "Non-trainable params: 0 (0.00 Byte)\n", | |
| "_________________________________________________________________\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "gru = gru_model(max_len)\n", | |
| "gru.summary()\n", | |
| "# Complie the models\n", | |
| "gru.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "m5zd-TmeBb5p", | |
| "outputId": "0907d4cb-5675-4cbd-e6ea-fc08bb9c6368" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Model: \"sequential_2\"\n", | |
| "_________________________________________________________________\n", | |
| " Layer (type) Output Shape Param # \n", | |
| "=================================================================\n", | |
| " embedding_2 (Embedding) (None, 32, 32) 16000 \n", | |
| " \n", | |
| " gru_2 (GRU) (None, 32, 128) 62208 \n", | |
| " \n", | |
| " dropout_4 (Dropout) (None, 32, 128) 0 \n", | |
| " \n", | |
| " gru_3 (GRU) (None, 32) 15552 \n", | |
| " \n", | |
| " dropout_5 (Dropout) (None, 32) 0 \n", | |
| " \n", | |
| " dense_2 (Dense) (None, 3) 99 \n", | |
| " \n", | |
| "=================================================================\n", | |
| "Total params: 93859 (366.64 KB)\n", | |
| "Trainable params: 93859 (366.64 KB)\n", | |
| "Non-trainable params: 0 (0.00 Byte)\n", | |
| "_________________________________________________________________\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Train the models" | |
| ], | |
| "metadata": { | |
| "id": "mSijiLQZ0dk3" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Train the models with early stopping\n", | |
| "early_stopping = EarlyStopping(patience=3, restore_best_weights=True)" | |
| ], | |
| "metadata": { | |
| "id": "w72HRLgx4e4Q" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "lstm_history = lstm.fit(train_padded, train_labels, epochs=10, validation_split=0.2)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "BxLCU34-4gtj", | |
| "outputId": "d2ccbde3-5a44-446c-ced1-d1f180276c1d" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Epoch 1/10\n", | |
| "687/687 [==============================] - 32s 37ms/step - loss: 1.0219 - accuracy: 0.4775 - val_loss: 0.8188 - val_accuracy: 0.6527\n", | |
| "Epoch 2/10\n", | |
| "687/687 [==============================] - 23s 34ms/step - loss: 0.8102 - accuracy: 0.6598 - val_loss: 0.7817 - val_accuracy: 0.6652\n", | |
| "Epoch 3/10\n", | |
| "687/687 [==============================] - 25s 37ms/step - loss: 0.7768 - accuracy: 0.6731 - val_loss: 0.7642 - val_accuracy: 0.6678\n", | |
| "Epoch 4/10\n", | |
| "687/687 [==============================] - 24s 35ms/step - loss: 0.7589 - accuracy: 0.6784 - val_loss: 0.7515 - val_accuracy: 0.6765\n", | |
| "Epoch 5/10\n", | |
| "687/687 [==============================] - 23s 34ms/step - loss: 0.7517 - accuracy: 0.6802 - val_loss: 0.7628 - val_accuracy: 0.6752\n", | |
| "Epoch 6/10\n", | |
| "687/687 [==============================] - 24s 35ms/step - loss: 0.7412 - accuracy: 0.6848 - val_loss: 0.7506 - val_accuracy: 0.6809\n", | |
| "Epoch 7/10\n", | |
| "687/687 [==============================] - 23s 34ms/step - loss: 0.7348 - accuracy: 0.6894 - val_loss: 0.7580 - val_accuracy: 0.6674\n", | |
| "Epoch 8/10\n", | |
| "687/687 [==============================] - 23s 34ms/step - loss: 0.7282 - accuracy: 0.6905 - val_loss: 0.7830 - val_accuracy: 0.6590\n", | |
| "Epoch 9/10\n", | |
| "687/687 [==============================] - 23s 34ms/step - loss: 0.7207 - accuracy: 0.6943 - val_loss: 0.7708 - val_accuracy: 0.6661\n", | |
| "Epoch 10/10\n", | |
| "687/687 [==============================] - 23s 34ms/step - loss: 0.7152 - accuracy: 0.6964 - val_loss: 0.7541 - val_accuracy: 0.6774\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "gru_history= gru.fit(train_padded, train_labels, epochs=10, validation_split=0.2, callbacks=[early_stopping])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "u8js0eVyBut3", | |
| "outputId": "bf75fabb-ff65-4b7d-d179-c655714fceb8" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Epoch 1/10\n", | |
| "687/687 [==============================] - 40s 52ms/step - loss: 1.0896 - accuracy: 0.3977 - val_loss: 1.0874 - val_accuracy: 0.4127\n", | |
| "Epoch 2/10\n", | |
| "687/687 [==============================] - 38s 55ms/step - loss: 1.0894 - accuracy: 0.4025 - val_loss: 1.0858 - val_accuracy: 0.4127\n", | |
| "Epoch 3/10\n", | |
| "687/687 [==============================] - 35s 50ms/step - loss: 1.0880 - accuracy: 0.4026 - val_loss: 1.0870 - val_accuracy: 0.4127\n", | |
| "Epoch 4/10\n", | |
| "687/687 [==============================] - 38s 55ms/step - loss: 1.0884 - accuracy: 0.4028 - val_loss: 1.0857 - val_accuracy: 0.4127\n", | |
| "Epoch 5/10\n", | |
| "687/687 [==============================] - 35s 51ms/step - loss: 1.0883 - accuracy: 0.4025 - val_loss: 1.0855 - val_accuracy: 0.4127\n", | |
| "Epoch 6/10\n", | |
| "687/687 [==============================] - 37s 54ms/step - loss: 0.9598 - accuracy: 0.5236 - val_loss: 0.7822 - val_accuracy: 0.6658\n", | |
| "Epoch 7/10\n", | |
| "687/687 [==============================] - 35s 51ms/step - loss: 0.7834 - accuracy: 0.6618 - val_loss: 0.7622 - val_accuracy: 0.6738\n", | |
| "Epoch 8/10\n", | |
| "687/687 [==============================] - 37s 54ms/step - loss: 0.7639 - accuracy: 0.6724 - val_loss: 0.7650 - val_accuracy: 0.6721\n", | |
| "Epoch 9/10\n", | |
| "687/687 [==============================] - 35s 51ms/step - loss: 0.7415 - accuracy: 0.6831 - val_loss: 0.7536 - val_accuracy: 0.6734\n", | |
| "Epoch 10/10\n", | |
| "687/687 [==============================] - 37s 53ms/step - loss: 0.7278 - accuracy: 0.6897 - val_loss: 0.7486 - val_accuracy: 0.6772\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def jaccard(str1, str2):\n", | |
| " a = set(str1.lower().split())\n", | |
| " b = set(str2.lower().split())\n", | |
| " c = a.intersection(b)\n", | |
| " return float(len(c)) / (len(a) + len(b) - len(c))\n", | |
| "\n", | |
| "def evaluate_model(model, tokenizer, data, true_texts):\n", | |
| " \"\"\"\n", | |
| " Evaluate the model using the Jaccard score.\n", | |
| " - model: The trained model (LSTM or GRU)\n", | |
| " - tokenizer: Tokenizer used for the model\n", | |
| " - data: The input data for prediction (features)\n", | |
| " - true_texts: The true output texts (labels)\n", | |
| "\n", | |
| " Returns the average Jaccard score for the dataset.\n", | |
| " \"\"\"\n", | |
| " # Generate predictions\n", | |
| " predictions = model.predict(data)\n", | |
| "\n", | |
| " pred_texts = [\" \".join(tokenizer.sequences_to_texts([p])) for p in predictions]\n", | |
| "\n", | |
| " # Compute Jaccard scores\n", | |
| " scores = [jaccard(pred, true) for pred, true in zip(pred_texts, true_texts)]\n", | |
| "\n", | |
| " # Return the average Jaccard score\n", | |
| " return sum(scores) / len(scores)" | |
| ], | |
| "metadata": { | |
| "id": "UGLCcoKzBuDm" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Evaluate on test data\n", | |
| "test_loss, test_acc = lstm.evaluate(test_padded, test_labels)\n", | |
| "print(\"Test accuracy:\", test_acc)\n", | |
| "\n", | |
| "test_loss, test_acc = gru.evaluate(test_padded, test_labels)\n", | |
| "print(\"Test accuracy:\", test_acc)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ZVbw5hb6C9Yg", | |
| "outputId": "179c13e3-64bf-4117-8a44-c0e35978fa87" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "111/111 [==============================] - 2s 14ms/step - loss: 0.7518 - accuracy: 0.6672\n", | |
| "Test accuracy: 0.6672325730323792\n", | |
| "111/111 [==============================] - 2s 16ms/step - loss: 0.7480 - accuracy: 0.6692\n", | |
| "Test accuracy: 0.6692133545875549\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Model accuracy\n", | |
| "lstm_val_acc = max(lstm_history.history['val_accuracy'])\n", | |
| "gru_val_acc = max(gru_history.history['val_accuracy'])\n", | |
| "\n", | |
| "print(\"LSTM validation accuracy:\", lstm_val_acc)\n", | |
| "print(\"GRU validation accuracy:\", gru_val_acc)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "fTkR4aemFpXS", | |
| "outputId": "bf7b8065-4a28-44fb-ffe3-f006b7dd9fdc" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "LSTM validation accuracy: 0.6808587908744812\n", | |
| "GRU validation accuracy: 0.677219808101654\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Confusion matrix\n", | |
| "from sklearn.metrics import classification_report, confusion_matrix\n", | |
| "lstm_pred = lstm.predict(test_padded)\n", | |
| "gru_pred = gru.predict(test_padded)\n", | |
| "\n", | |
| "print(classification_report(test_labels.argmax(axis=1), lstm_pred.argmax(axis=1)))\n", | |
| "print(classification_report(test_labels.argmax(axis=1), gru_pred.argmax(axis=1)))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Vu1OEfmuMd5S", | |
| "outputId": "b866ec81-2d9f-440a-9cf5-3eadacc159bc" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "111/111 [==============================] - 2s 11ms/step\n", | |
| "111/111 [==============================] - 2s 14ms/step\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.78 0.46 0.58 1001\n", | |
| " 1 0.58 0.79 0.67 1430\n", | |
| " 2 0.76 0.70 0.73 1103\n", | |
| "\n", | |
| " accuracy 0.67 3534\n", | |
| " macro avg 0.71 0.65 0.66 3534\n", | |
| "weighted avg 0.70 0.67 0.66 3534\n", | |
| "\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.75 0.47 0.58 1001\n", | |
| " 1 0.58 0.79 0.67 1430\n", | |
| " 2 0.79 0.69 0.74 1103\n", | |
| "\n", | |
| " accuracy 0.67 3534\n", | |
| " macro avg 0.71 0.65 0.66 3534\n", | |
| "weighted avg 0.69 0.67 0.67 3534\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "25XNKkNYCQae" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from datetime import datetime\n", | |
| "\n", | |
| "def save_model(model, prefix =''):\n", | |
| " # Get the current time and filename\n", | |
| " current_time = datetime.now().strftime(\"%Y-%m-%d-%H%M%S\")\n", | |
| " filename = f\"{prefix}model_{current_time}.csv\"\n", | |
| " # Save the models\n", | |
| " model.save(filename)\n", | |
| " print(f\"Model saved to {filename}\")\n", | |
| "\n", | |
| "save_model(lstm, prefix = \"lstm\")\n", | |
| "save_model(gru, prefix = \"gru\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "LYqnTQa-bdqn", | |
| "outputId": "e248dd33-fa4a-4c52-88c2-01d216316d18" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Model saved to lstmmodel_2024-03-04-015021.csv\n", | |
| "Model saved to grumodel_2024-03-04-015029.csv\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install --upgrade kaggle" | |
| ], | |
| "metadata": { | |
| "id": "vgjLVo-l_LO9" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! kaggle competitions submit -c tweet-sentiment-extraction -f '/content/lstmmodel_2024-03-04-005517.csv' -m late-submission" | |
| ], | |
| "metadata": { | |
| "id": "kmaX09fo9Urz", | |
| "outputId": "24134994-354d-4d1b-8ac2-9e7ab86e3994", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Traceback (most recent call last):\n", | |
| " File \"/usr/local/bin/kaggle\", line 8, in <module>\n", | |
| " sys.exit(main())\n", | |
| " File \"/usr/local/lib/python3.10/dist-packages/kaggle/cli.py\", line 70, in main\n", | |
| " out = args.func(**command_args)\n", | |
| " File \"/usr/local/lib/python3.10/dist-packages/kaggle/api/kaggle_api_extended.py\", line 801, in competition_submit_cli\n", | |
| " submit_result = self.competition_submit(file_name, message,\n", | |
| " File \"/usr/local/lib/python3.10/dist-packages/kaggle/api/kaggle_api_extended.py\", line 752, in competition_submit\n", | |
| " content_length=os.path.getsize(file_name),\n", | |
| " File \"/usr/lib/python3.10/genericpath.py\", line 50, in getsize\n", | |
| " return os.stat(filename).st_size\n", | |
| "FileNotFoundError: [Errno 2] No such file or directory: '/content/lstmmodel_2024-03-04-005517.csv'\n" | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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