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asvnpr/topic_modeling-demo.ipynb

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topic_modeling-demo.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
},
"colab": {
"name": "topic_modeling demo.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/asvnpr/edc04141f7e0718551b54876c95803de/topic_modeling-demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wehaECjm8Z4t",
"colab_type": "text"
},
"source": [
"# Service Text Content : Topic Model\n",
"\n",
"See how the text is spread in our data through topic modeling\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "a73qer_zPJLy",
"colab_type": "code",
"outputId": "44991665-07fd-48db-caa6-41132b087350",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 515
}
},
"source": [
"!pip3 install gdown tqdm pyldavis"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: gdown in /usr/local/lib/python3.6/dist-packages (3.6.4)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (4.28.1)\n",
"Requirement already satisfied: pyldavis in /usr/local/lib/python3.6/dist-packages (2.1.2)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gdown) (1.12.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from gdown) (2.21.0)\n",
"Requirement already satisfied: numexpr in /usr/local/lib/python3.6/dist-packages (from pyldavis) (2.7.1)\n",
"Requirement already satisfied: pytest in /usr/local/lib/python3.6/dist-packages (from pyldavis) (3.6.4)\n",
"Requirement already satisfied: pandas>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.25.3)\n",
"Requirement already satisfied: joblib>=0.8.4 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.14.1)\n",
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"Requirement already satisfied: jinja2>=2.7.2 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (2.11.1)\n",
"Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (1.24.3)\n",
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"Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.3.0)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (45.1.0)\n",
"Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (0.7.1)\n",
"Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (19.3.0)\n",
"Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.8.1)\n",
"Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (8.2.0)\n",
"Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.0->pyldavis) (2.6.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.0->pyldavis) (2018.9)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.7.2->pyldavis) (1.1.1)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CXWz_ZR5cQhO",
"colab_type": "text"
},
"source": [
"## GSDMM Topic Modeling \n",
"( Gibbs Sampling\n",
"algorithm for the Dirichlet Multinomial Mixture model for\n",
"short text clustering) \n",
"Designed for short texts. Converges relatively quickly. [See paper](http://dbgroup.cs.tsinghua.edu.cn/wangjy/papers/KDD14-GSDMM.pdf). Following this [guide](https://towardsdatascience.com/short-text-topic-modeling-70e50a57c883)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "c2vAWxuGeKi9",
"colab_type": "code",
"colab": {}
},
"source": [
"# following this writeup: https://towardsdatascience.com/short-text-topic-modeling-70e50a57c883\n",
"\n",
"# STTM lib from Github\n",
"\n",
"import pyLDAvis\n",
"import json\n",
"import os\n",
"from tqdm import tqdm, trange\n",
"# import pandas as pd\n",
"# import numpy as np\n",
"import gdown"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "_JWQwgp7tHi_",
"colab_type": "text"
},
"source": [
"### Download GSDMM Topic Model visualization and display"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0SMtUDYgh6yU",
"colab_type": "code",
"outputId": "0f269a02-0dd1-49f2-cb3a-7211afb4cfc5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
}
},
"source": [
"vis_file = 'pyLDAvis.json'\n",
"vis_url = 'https://drive.google.com/uc?id=1AUCu7QC85xkdaJebWVwbxw5DX-d0qyqy'\n",
"gdown.download(vis_url, vis_file, quiet=False)\n",
"\n",
"if os.path.exists(vis_file):\n",
" with open(vis_file) as f:\n",
" prep_data = json.load(f)\n",
" ldavis_prep_data = pyLDAvis.PreparedData(\n",
" topic_coordinates=prep_data['mdsDat'],\n",
" topic_info=prep_data['tinfo'],\n",
" token_table=prep_data['token.table'],\n",
" R=prep_data['R'],\n",
" lambda_step=prep_data['lambda.step'],\n",
" plot_opts=prep_data['plot.opts'],\n",
" topic_order=prep_data['topic.order']\n",
" )\n",
" def to_dict():\n",
" return prep_data\n",
" ldavis_prep_data.to_dict = to_dict\n",
"else:\n",
" print('ERROR! Visualization data file does not exist...')\n"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1AUCu7QC85xkdaJebWVwbxw5DX-d0qyqy\n",
"To: /content/pyLDAvis.json\n",
"100%|██████████| 392k/392k [00:00<00:00, 61.4MB/s]\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gnBhOnPH187e",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 861
},
"outputId": "319d6eb8-a78d-4958-9c20-05e4c45f86f2"
},
"source": [
"pyLDAvis.display(ldavis_prep_data)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
"<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
"\n",
"\n",
"<div id=\"ldavis_el8371405438089155206449921432\"></div>\n",
"<script type=\"text/javascript\">\n",
"\n",
"var ldavis_el8371405438089155206449921432_data = {\"mdsDat\": {\"x\": [-157.0683135986328, -29.18461799621582, 75.21026611328125, -136.37696838378906, 13.511815071105957, -76.70690155029297, -99.0778579711914, 137.46820068359375, -43.97426986694336, -43.669185638427734, 0.620433509349823, 70.11038208007812, -100.59305572509766, 75.86831665039062, 87.5549545288086, -69.06086730957031, 115.11629486083984, 30.58905029296875, -160.81356811523438, 138.0100555419922, 15.351123809814453, 10.904839515686035, 39.229736328125, -88.58614349365234, -28.109970092773438], \"y\": [-10.944620132446289, 119.14608001708984, 55.861595153808594, -86.61174774169922, 44.31654739379883, 168.01365661621094, 100.30857849121094, 7.732316493988037, 2.4529268741607666, 61.07630920410156, -126.78361511230469, -5.224271297454834, 29.705629348754883, -122.7475814819336, 137.25604248046875, -117.64570617675781, -59.610435485839844, 101.8108901977539, 68.44844818115234, 80.57588958740234, 174.06019592285156, 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"\n",
"function LDAvis_load_lib(url, callback){\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = true;\n",
" s.onreadystatechange = s.onload = callback;\n",
" s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
"}\n",
"\n",
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" new LDAvis(\"#\" + \"ldavis_el8371405438089155206449921432\", ldavis_el8371405438089155206449921432_data);\n",
" }(LDAvis);\n",
"}else if(typeof define === \"function\" && define.amd){\n",
" // require.js is available: use it to load d3/LDAvis\n",
" require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
" require([\"d3\"], function(d3){\n",
" window.d3 = d3;\n",
" LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
" new LDAvis(\"#\" + \"ldavis_el8371405438089155206449921432\", ldavis_el8371405438089155206449921432_data);\n",
" });\n",
" });\n",
"}else{\n",
" // require.js not available: dynamically load d3 & LDAvis\n",
" LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
" LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
" new LDAvis(\"#\" + \"ldavis_el8371405438089155206449921432\", ldavis_el8371405438089155206449921432_data);\n",
" })\n",
" });\n",
"}\n",
"</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
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},
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}
]
}
]
}
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