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newsgroups.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['rec.autos' 'comp.sys.mac.hardware' 'comp.graphics' 'sci.space'\n",
" 'talk.politics.guns' 'sci.med' 'comp.sys.ibm.pc.hardware'\n",
" 'comp.os.ms-windows.misc' 'rec.motorcycles' 'talk.religion.misc'\n",
" 'misc.forsale' 'alt.atheism' 'sci.electronics' 'comp.windows.x'\n",
" 'rec.sport.hockey' 'rec.sport.baseball' 'soc.religion.christian'\n",
" 'talk.politics.mideast' 'talk.politics.misc' 'sci.crypt']\n"
]
},
{
"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>content</th>\n",
" <th>target</th>\n",
" <th>target_names</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>From: lerxst@wam.umd.edu (where's my thing)\\nS...</td>\n",
" <td>7</td>\n",
" <td>rec.autos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>From: guykuo@carson.u.washington.edu (Guy Kuo)...</td>\n",
" <td>4</td>\n",
" <td>comp.sys.mac.hardware</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>From: twillis@ec.ecn.purdue.edu (Thomas E Will...</td>\n",
" <td>4</td>\n",
" <td>comp.sys.mac.hardware</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>From: jgreen@amber (Joe Green)\\nSubject: Re: W...</td>\n",
" <td>1</td>\n",
" <td>comp.graphics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>From: jcm@head-cfa.harvard.edu (Jonathan McDow...</td>\n",
" <td>14</td>\n",
" <td>sci.space</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" content target \\\n",
"0 From: lerxst@wam.umd.edu (where's my thing)\\nS... 7 \n",
"1 From: guykuo@carson.u.washington.edu (Guy Kuo)... 4 \n",
"2 From: twillis@ec.ecn.purdue.edu (Thomas E Will... 4 \n",
"3 From: jgreen@amber (Joe Green)\\nSubject: Re: W... 1 \n",
"4 From: jcm@head-cfa.harvard.edu (Jonathan McDow... 14 \n",
"\n",
" target_names \n",
"0 rec.autos \n",
"1 comp.sys.mac.hardware \n",
"2 comp.sys.mac.hardware \n",
"3 comp.graphics \n",
"4 sci.space "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_json('https://raw.githubusercontent.com/selva86/datasets/master/newsgroups.json')\n",
"print(df.target_names.unique())\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From: (Guy Kuo) Subject: SI Clock Poll - Final Call Summary: Final call for SI clock reports Keywords: SI,acceleration,clock,upgrade Article-I.D.: shelley.1qvfo9INNc3s Organization: University of Washington Lines: 11 NNTP-Posting-Host: carson.u.washington.edu A fair number of brave souls who upgraded their SI clock oscillator have shared their experiences for this poll. Please send a brief message detailing your experiences with the procedure. Top speed attained, CPU rated speed, add on cards and adapters, heat sinks, hour of usage per day, floppy disk functionality with 800 and 1.4 m floppies are especially requested. I will be summarizing in the next two days, so please add to the network knowledge base if you have done the clock upgrade and havent answered this poll. Thanks. Guy Kuo \n"
]
}
],
"source": [
"import re\n",
" \n",
"text_corpus = df.content.values.tolist()\n",
"text_corpus = [re.sub('\\S*@\\S*\\s?', '', doc) for doc in text_corpus] #removing email addresses\n",
"text_corpus = [re.sub('\\s+', ' ', doc) for doc in text_corpus] #removing newline characters\n",
"text_corpus = [re.sub(\"\\'\", \"\", doc) for doc in text_corpus] #removing single quote characters\n",
" \n",
"print(text_corpus[1])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['from', 'guy', 'kuo', 'subject', 'si', 'clock', 'poll', 'final', 'call', 'summary', 'final', 'call', 'for', 'si', 'clock', 'reports', 'keywords', 'si', 'acceleration', 'clock', 'upgrade', 'article', 'shelley', 'qvfo', 'innc', 'organization', 'university', 'of', 'washington', 'lines', 'nntp', 'posting', 'host', 'carson', 'washington', 'edu', 'fair', 'number', 'of', 'brave', 'souls', 'who', 'upgraded', 'their', 'si', 'clock', 'oscillator', 'have', 'shared', 'their', 'experiences', 'for', 'this', 'poll', 'please', 'send', 'brief', 'message', 'detailing', 'your', 'experiences', 'with', 'the', 'procedure', 'top', 'speed', 'attained', 'cpu', 'rated', 'speed', 'add', 'on', 'cards', 'and', 'adapters', 'heat', 'sinks', 'hour', 'of', 'usage', 'per', 'day', 'floppy', 'disk', 'functionality', 'with', 'and', 'floppies', 'are', 'especially', 'requested', 'will', 'be', 'summarizing', 'in', 'the', 'next', 'two', 'days', 'so', 'please', 'add', 'to', 'the', 'network', 'knowledge', 'base', 'if', 'you', 'have', 'done', 'the', 'clock', 'upgrade', 'and', 'havent', 'answered', 'this', 'poll', 'thanks', 'guy', 'kuo']\n"
]
}
],
"source": [
"import gensim\n",
"import warnings\n",
"warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
" \n",
"def doc_to_words(sentences):\n",
" for sentence in sentences:\n",
" yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))\n",
" \n",
"words = list(doc_to_words(text_corpus))\n",
" \n",
"print(words[1])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['guy', 'kuo', 'si', 'clock', 'poll', 'final', 'call', 'summary', 'final', 'call', 'si', 'clock', 'reports', 'keywords', 'si', 'acceleration', 'clock', 'upgrade', 'article', 'shelley', 'qvfo', 'innc', 'organization', 'university', 'washington', 'lines', 'nntp', 'posting', 'host', 'carson', 'washington', 'fair', 'number', 'brave', 'souls', 'upgraded', 'si', 'clock', 'oscillator', 'shared', 'experiences', 'poll', 'please', 'send', 'brief', 'message', 'detailing', 'experiences', 'procedure', 'top', 'speed', 'attained', 'cpu', 'rated', 'speed', 'add', 'cards', 'adapters', 'heat', 'sinks', 'hour', 'usage', 'per', 'day', 'floppy', 'disk', 'functionality', 'floppies', 'especially', 'requested', 'summarizing', 'next', 'two', 'days', 'please', 'add', 'network', 'knowledge', 'base', 'done', 'clock', 'upgrade', 'havent', 'answered', 'poll', 'thanks', 'guy', 'kuo']\n"
]
}
],
"source": [
"from nltk.corpus import stopwords\n",
"stop_words = stopwords.words('english')\n",
"stop_words.extend(['from', 'subject', 're', 'edu', 'use'])\n",
" \n",
"def remove_stopwords(text):\n",
" return [[word for word in gensim.utils.simple_preprocess(str(doc)) if word not in stop_words] for doc in text_corpus]\n",
" \n",
"words = remove_stopwords(words)\n",
" \n",
"print(words[1])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 documents lemmatised\n",
"500 documents lemmatised\n",
"1000 documents lemmatised\n",
"1500 documents lemmatised\n",
"2000 documents lemmatised\n",
"2500 documents lemmatised\n",
"3000 documents lemmatised\n",
"3500 documents lemmatised\n",
"4000 documents lemmatised\n",
"4500 documents lemmatised\n",
"5000 documents lemmatised\n",
"5500 documents lemmatised\n",
"6000 documents lemmatised\n",
"6500 documents lemmatised\n",
"7000 documents lemmatised\n",
"7500 documents lemmatised\n",
"8000 documents lemmatised\n",
"8500 documents lemmatised\n",
"9000 documents lemmatised\n",
"9500 documents lemmatised\n",
"10000 documents lemmatised\n",
"10500 documents lemmatised\n",
"11000 documents lemmatised\n"
]
}
],
"source": [
"import spacy\n",
"nlp = spacy.load('en', disable=['parser', 'ner'])\n",
"\n",
"def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
" texts_out = []\n",
" for idx, sent in enumerate(texts):\n",
" if (idx) % 500 == 0:\n",
" print(str(idx) + ' documents lemmatised')\n",
" doc = nlp(\" \".join(sent)) \n",
" texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
" return texts_out\n",
"\n",
"data_lemmatized = lemmatization(words, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# Create Dictionary\n",
"import gensim.corpora as corpora\n",
"id2word = corpora.Dictionary(data_lemmatized)\n",
"\n",
"# Create Corpus\n",
"corpus = [id2word.doc2bow(text) for text in data_lemmatized]\n",
"\n",
"# Build LDA model\n",
"lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
" id2word=id2word,\n",
" num_topics=20, \n",
" per_word_topics=True)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0,\n",
" '0.021*\"line\" + 0.015*\"car\" + 0.015*\"post\" + 0.013*\"nntp\" + 0.011*\"host\" + '\n",
" '0.010*\"get\" + 0.010*\"price\" + 0.009*\"be\" + 0.009*\"power\" + 0.008*\"new\"'),\n",
" (1,\n",
" '0.008*\"write\" + 0.007*\"post\" + 0.007*\"line\" + 0.007*\"good\" + 0.006*\"be\" + '\n",
" '0.006*\"make\" + 0.006*\"article\" + 0.006*\"question\" + 0.006*\"also\" + '\n",
" '0.005*\"problem\"'),\n",
" (2,\n",
" '0.016*\"write\" + 0.010*\"go\" + 0.010*\"line\" + 0.009*\"article\" + 0.009*\"be\" + '\n",
" '0.008*\"post\" + 0.008*\"think\" + 0.006*\"people\" + 0.006*\"make\" + 0.006*\"say\"'),\n",
" (3,\n",
" '0.023*\"gun\" + 0.013*\"people\" + 0.009*\"man\" + 0.008*\"write\" + '\n",
" '0.006*\"firearm\" + 0.006*\"make\" + 0.005*\"crime\" + 0.005*\"study\" + '\n",
" '0.005*\"homosexual\" + 0.005*\"number\"'),\n",
" (4,\n",
" '0.402*\"ax\" + 0.008*\"militia\" + 0.005*\"tm\" + 0.004*\"write\" + 0.004*\"beep\" + '\n",
" '0.003*\"know\" + 0.003*\"right\" + 0.003*\"tq\" + 0.003*\"say\" + 0.003*\"people\"'),\n",
" (5,\n",
" '0.017*\"people\" + 0.016*\"say\" + 0.011*\"believe\" + 0.008*\"know\" + '\n",
" '0.008*\"write\" + 0.008*\"come\" + 0.007*\"think\" + 0.007*\"go\" + 0.007*\"see\" + '\n",
" '0.006*\"right\"'),\n",
" (6,\n",
" '0.019*\"write\" + 0.013*\"article\" + 0.012*\"line\" + 0.009*\"post\" + '\n",
" '0.008*\"year\" + 0.008*\"think\" + 0.007*\"know\" + 0.007*\"nntp\" + 0.007*\"say\" + '\n",
" '0.006*\"make\"'),\n",
" (7,\n",
" '0.017*\"file\" + 0.012*\"drive\" + 0.012*\"use\" + 0.011*\"system\" + '\n",
" '0.011*\"window\" + 0.008*\"line\" + 0.008*\"program\" + 0.008*\"disk\" + '\n",
" '0.007*\"work\" + 0.007*\"run\"'),\n",
" (8,\n",
" '0.011*\"get\" + 0.011*\"go\" + 0.011*\"write\" + 0.009*\"be\" + 0.009*\"line\" + '\n",
" '0.009*\"see\" + 0.008*\"year\" + 0.007*\"say\" + 0.007*\"take\" + 0.006*\"time\"'),\n",
" (9,\n",
" '0.015*\"list\" + 0.014*\"book\" + 0.013*\"send\" + 0.012*\"include\" + 0.012*\"mail\" '\n",
" '+ 0.008*\"address\" + 0.008*\"post\" + 0.008*\"available\" + 0.007*\"copy\" + '\n",
" '0.007*\"information\"'),\n",
" (10,\n",
" '0.018*\"key\" + 0.012*\"government\" + 0.009*\"law\" + 0.008*\"public\" + '\n",
" '0.006*\"right\" + 0.006*\"encryption\" + 0.006*\"state\" + 0.006*\"people\" + '\n",
" '0.005*\"chip\" + 0.005*\"make\"'),\n",
" (11,\n",
" '0.041*\"armenian\" + 0.035*\"turkish\" + 0.020*\"turk\" + 0.012*\"event\" + '\n",
" '0.011*\"greek\" + 0.010*\"genocide\" + 0.010*\"people\" + 0.008*\"muslim\" + '\n",
" '0.007*\"mountain\" + 0.006*\"population\"'),\n",
" (12,\n",
" '0.023*\"team\" + 0.012*\"game\" + 0.012*\"write\" + 0.011*\"year\" + 0.011*\"player\" '\n",
" '+ 0.011*\"think\" + 0.010*\"say\" + 0.010*\"play\" + 0.008*\"good\" + '\n",
" '0.008*\"season\"'),\n",
" (13,\n",
" '0.009*\"space\" + 0.008*\"write\" + 0.008*\"launch\" + 0.007*\"orbit\" + '\n",
" '0.007*\"use\" + 0.007*\"line\" + 0.007*\"system\" + 0.006*\"time\" + 0.006*\"speed\" '\n",
" '+ 0.005*\"program\"'),\n",
" (14,\n",
" '0.017*\"rate\" + 0.010*\"problem\" + 0.008*\"insurance\" + 0.007*\"bike\" + '\n",
" '0.007*\"write\" + 0.007*\"homicide\" + 0.006*\"purchase\" + 0.006*\"gun\" + '\n",
" '0.006*\"get\" + 0.005*\"know\"'),\n",
" (15,\n",
" '0.022*\"church\" + 0.008*\"worship\" + 0.008*\"appear\" + 0.007*\"sin\" + '\n",
" '0.007*\"know\" + 0.007*\"say\" + 0.007*\"marriage\" + 0.007*\"scripture\" + '\n",
" '0.007*\"law\" + 0.006*\"also\"'),\n",
" (16,\n",
" '0.010*\"slave\" + 0.008*\"cool\" + 0.007*\"word\" + 0.007*\"say\" + 0.007*\"nuclear\" '\n",
" '+ 0.006*\"water\" + 0.006*\"law\" + 0.006*\"know\" + 0.006*\"write\" + '\n",
" '0.005*\"make\"'),\n",
" (17,\n",
" '0.013*\"say\" + 0.008*\"make\" + 0.008*\"know\" + 0.008*\"think\" + 0.008*\"people\" '\n",
" '+ 0.007*\"write\" + 0.006*\"go\" + 0.005*\"kill\" + 0.005*\"time\" + 0.005*\"thing\"'),\n",
" (18,\n",
" '0.027*\"line\" + 0.020*\"post\" + 0.014*\"nntp\" + 0.012*\"host\" + 0.012*\"thank\" + '\n",
" '0.010*\"know\" + 0.010*\"write\" + 0.009*\"need\" + 0.009*\"use\" + 0.009*\"card\"'),\n",
" (19,\n",
" '0.016*\"motif\" + 0.011*\"line\" + 0.011*\"amp\" + 0.011*\"application\" + '\n",
" '0.009*\"cell\" + 0.009*\"picture\" + 0.008*\"sleeve\" + 0.008*\"adcom\" + '\n",
" '0.006*\"write\" + 0.006*\"disc\"')]\n"
]
}
],
"source": [
"from pprint import pprint\n",
"pprint(lda_model.print_topics())\n",
"doc_lda = lda_model[corpus]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/breadchris/projects/pocket_clone/pocket_data/env/lib/python3.8/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
"of pandas will change to not sort by default.\n",
"\n",
"To accept the future behavior, pass 'sort=False'.\n",
"\n",
"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
"\n",
" return pd.concat([default_term_info] + list(topic_dfs))\n"
]
},
{
"data": {
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"\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_el2994861398254952430083053112277\"></div>\n",
"<script type=\"text/javascript\">\n",
"\n",
"var ldavis_el2994861398254952430083053112277_data = {\"mdsDat\": {\"x\": [0.10581359491665082, -0.1770047593282785, -0.001833282271534274, 0.012807978699133252, 0.08166580636828075, -0.01308287121786891, -0.159917003732246, 0.05812774719535468, -0.12288306203188938, 0.07398924130593333, -0.08305675187641343, -0.031479932310264865, 0.03519350684063547, -0.0960635903876282, 0.07125985960252873, 0.042388167531187335, 0.14941249891992123, 0.13872566535133263, -0.020194718832795062, -0.06386809474204012], \"y\": [-0.08661201848043364, 0.01312283729854125, -0.06441164194303868, -0.04966568737602264, -0.05737600775617184, -0.07272256346514083, -0.004623983153403142, -0.10835296428110298, -0.041612496284403305, -0.05150613059586243, -0.006825495812602893, -0.0352912945604909, -0.06895476686836345, 0.07456689313304457, 0.028174518960468315, 0.0016170270491429899, 0.14921744376199397, 0.12258470384896218, 0.06988601880761378, 0.1887856077172698], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [10.300811767578125, 10.06515884399414, 9.71107006072998, 9.352972030639648, 9.044584274291992, 7.797872066497803, 7.484724521636963, 5.097229957580566, 4.6858978271484375, 4.408182144165039, 4.363588809967041, 4.147014617919922, 4.0915303230285645, 2.6594607830047607, 1.745190143585205, 1.5440832376480103, 1.2655584812164307, 1.0845165252685547, 0.8335022330284119, 0.31704673171043396]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", 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\"worship\", \"worship\", \"worship\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"xor\", \"xor\", \"xputimage\", \"xputimage\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yearly\", \"zionism\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [6, 8, 3, 11, 18, 9, 19, 13, 1, 4, 14, 2, 7, 10, 16, 17, 12, 5, 15, 20]};\n",
"\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",
"if(typeof(LDAvis) !== \"undefined\"){\n",
" // already loaded: just create the visualization\n",
" !function(LDAvis){\n",
" new LDAvis(\"#\" + \"ldavis_el2994861398254952430083053112277\", ldavis_el2994861398254952430083053112277_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_el2994861398254952430083053112277\", ldavis_el2994861398254952430083053112277_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_el2994861398254952430083053112277\", ldavis_el2994861398254952430083053112277_data);\n",
" })\n",
" });\n",
"}\n",
"</script>"
],
"text/plain": [
"PreparedData(topic_coordinates= x y topics cluster Freq\n",
"topic \n",
"5 0.105814 -0.086612 1 1 10.300812\n",
"7 -0.177005 0.013123 2 1 10.065159\n",
"2 -0.001833 -0.064412 3 1 9.711070\n",
"10 0.012808 -0.049666 4 1 9.352972\n",
"17 0.081666 -0.057376 5 1 9.044584\n",
"8 -0.013083 -0.072723 6 1 7.797872\n",
"18 -0.159917 -0.004624 7 1 7.484725\n",
"12 0.058128 -0.108353 8 1 5.097230\n",
"0 -0.122883 -0.041612 9 1 4.685898\n",
"3 0.073989 -0.051506 10 1 4.408182\n",
"13 -0.083057 -0.006825 11 1 4.363589\n",
"1 -0.031480 -0.035291 12 1 4.147015\n",
"6 0.035194 -0.068955 13 1 4.091530\n",
"9 -0.096064 0.074567 14 1 2.659461\n",
"15 0.071260 0.028175 15 1 1.745190\n",
"16 0.042388 0.001617 16 1 1.544083\n",
"11 0.149412 0.149217 17 1 1.265558\n",
"4 0.138726 0.122585 18 1 1.084517\n",
"14 -0.020195 0.069886 19 1 0.833502\n",
"19 -0.063868 0.188786 20 1 0.317047, topic_info= Category Freq Term Total loglift logprob\n",
"3735 Default 5082.000000 ax 5082.000000 30.0000 30.0000\n",
"18 Default 10082.000000 line 10082.000000 29.0000 29.0000\n",
"691 Default 1726.000000 team 1726.000000 28.0000 28.0000\n",
"1500 Default 1578.000000 gun 1578.000000 27.0000 27.0000\n",
"1880 Default 842.000000 armenian 842.000000 26.0000 26.0000\n",
"... ... ... ... ... ... ...\n",
"103 Topic20 14.127934 get 5660.945312 -0.2393 -5.5564\n",
"23 Topic20 13.384160 nntp 4400.586426 -0.0415 -5.6105\n",
"184 Topic20 12.157939 system 3539.668945 0.0801 -5.7065\n",
"92 Topic20 11.768329 distribution 2509.490723 0.3915 -5.7391\n",
"681 Topic20 11.401780 source 1367.543701 0.9669 -5.7708\n",
"\n",
"[1649 rows x 6 columns], token_table= Topic Freq Term\n",
"term \n",
"7142 1 0.037161 _\n",
"7142 2 0.003097 _\n",
"7142 3 0.092903 _\n",
"7142 4 0.021677 _\n",
"7142 5 0.006194 _\n",
"... ... ... ...\n",
"37 18 0.004437 year\n",
"37 19 0.009656 year\n",
"37 20 0.000522 year\n",
"1002 6 0.923212 yearly\n",
"7384 3 0.948586 zionism\n",
"\n",
"[8950 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 8, 3, 11, 18, 9, 19, 13, 1, 4, 14, 2, 7, 10, 16, 17, 12, 5, 15, 20])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Visualize the topics\n",
"import pyLDAvis\n",
"import pyLDAvis.gensim \n",
"pyLDAvis.enable_notebook()\n",
"vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
"vis"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=text_corpus):\n",
" # Array of top 10 topics\n",
" top10array = []\n",
"\n",
" for row in range(ldamodel.num_topics):\n",
" wp = ldamodel.show_topic(row)\n",
" topic_keywords = \", \".join([word for word, prop in wp])\n",
" top10array.append((row+1, topic_keywords))\n",
"\n",
" top10dict = dict(top10array)\n",
"\n",
" sent_topics_df = pd.DataFrame(pd.DataFrame([sorted(topic[0], key=lambda x: (x[1]), reverse=True) for topic in ldamodel[corpus]])[0])\n",
" sent_topics_df.columns=[\"Data\"]\n",
" sent_topics_df['Dominant_Topic'] = sent_topics_df.Data.apply(lambda x: x[0]+1)\n",
" sent_topics_df['Perc_Contribution'] = sent_topics_df.Data.apply(lambda x: round(x[1],4))\n",
" sent_topics_df['Topic_Keywords'] = sent_topics_df.Dominant_Topic.apply(lambda x: top10dict[x])\n",
"\n",
" # Add original text to the end of the output\n",
" contents = pd.Series(texts)\n",
" sent_topics_df = pd.concat([sent_topics_df, contents.rename(\"Text\")], axis=1)\n",
" sent_topics_df = sent_topics_df[['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords', 'Text']]\n",
" return(sent_topics_df)\n",
"\n",
"df_topic_sents_keywords = format_topics_sentences()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"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>Topic_Num</th>\n",
" <th>Topic_Perc_Contrib</th>\n",
" <th>Keywords</th>\n",
" <th>Text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0.9912</td>\n",
" <td>line, car, post, nntp, host, get, price, be, p...</td>\n",
" <td>From: (John Whitmore) Subject: Re: A question ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.9917</td>\n",
" <td>write, post, line, good, be, make, article, qu...</td>\n",
" <td>From: (ANDREAS ARFF) Subject: Re: Newsgroup Sp...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.9951</td>\n",
" <td>write, go, line, article, be, post, think, peo...</td>\n",
" <td>From: (Jody Levine) Subject: Re: insect impact...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0.9922</td>\n",
" <td>gun, people, man, write, firearm, make, crime,...</td>\n",
" <td>From: (Lawrence C. Foard) Subject: Re: New Stu...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0.9994</td>\n",
" <td>ax, militia, tm, write, beep, know, right, tq,...</td>\n",
" <td>Subject: roman.bmp 12/14 From: (Cliff) Reply-T...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Topic_Num Topic_Perc_Contrib \\\n",
"0 1 0.9912 \n",
"1 2 0.9917 \n",
"2 3 0.9951 \n",
"3 4 0.9922 \n",
"4 5 0.9994 \n",
"\n",
" Keywords \\\n",
"0 line, car, post, nntp, host, get, price, be, p... \n",
"1 write, post, line, good, be, make, article, qu... \n",
"2 write, go, line, article, be, post, think, peo... \n",
"3 gun, people, man, write, firearm, make, crime,... \n",
"4 ax, militia, tm, write, beep, know, right, tq,... \n",
"\n",
" Text \n",
"0 From: (John Whitmore) Subject: Re: A question ... \n",
"1 From: (ANDREAS ARFF) Subject: Re: Newsgroup Sp... \n",
"2 From: (Jody Levine) Subject: Re: insect impact... \n",
"3 From: (Lawrence C. Foard) Subject: Re: New Stu... \n",
"4 Subject: roman.bmp 12/14 From: (Cliff) Reply-T... "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Group top 5 sentences under each topic\n",
"sent_topics_sorteddf_mallet = pd.DataFrame()\n",
"\n",
"sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n",
"\n",
"for i, grp in sent_topics_outdf_grpd:\n",
" sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
" grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n",
" axis=0)\n",
"\n",
"# Reset Index \n",
"sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
"\n",
"# Format\n",
"sent_topics_sorteddf_mallet.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Text\"]\n",
"\n",
"# Show\n",
"sent_topics_sorteddf_mallet.head()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "env",
"language": "python",
"name": "env"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.0"
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"nbformat_minor": 2
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