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AndradeEduardo/LDA_Modelling.ipynb

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LDA_Modelling.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Loading all deed files"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Text Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#modules for preprocessing the corpus by regular expressions\n",
"import re\n",
"import textacy\n",
"# module for preprocessing the corpus by parts of speach \n",
"import spacy \n",
"nlp = spacy.load('en')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Regular Expression Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def clean_corpus(sentence):\n",
" sentence = re.sub(r\"\\\\\\'\", r' ', sentence)\n",
" sentence = re.sub(r' s ', r\" \", sentence)\n",
" ## removing character sequences of Word files\n",
" sentence = re.sub(r'(\\\\n|\\\\xe2|\\\\xa2|\\\\x80|\\\\x9c|\\\\x9c|\\\\x9d|\\\\t|\\\\nr|\\\\x93)', r' ', sentence)\n",
" sentence = sentence.replace(r\"b'\", ' ') ## removing the beginning of some documents\n",
" sentence = re.sub(r'\\{[^\\}]+\\}', r' ', sentence) ## removing word macros\n",
"\n",
" sentence = textacy.preprocess.replace_emails(sentence, replace_with=r' ') ## removing emails\n",
" sentence = textacy.preprocess.replace_urls(sentence, replace_with=r' ') ## removing urls\n",
"\n",
" sentence = re.sub(r'(#|$| [b-z] |\\s[B-Z]\\s|\\sxxx\\s|\\sXXX\\s|XXX\\w+)', r' ', sentence)\n",
" \n",
" ## removing character sequences of pdf files\n",
" sentence = re.sub(r'(\\\\x01|\\\\x0c|\\\\x98|\\\\x99|\\\\xa6|\\\\xc2|\\\\xa0|\\\\xa9|\\\\x82)', r' ', sentence)\n",
"\n",
" sentence = re.sub(r'(c/-d*)', r' ', sentence) #remove address\n",
" \n",
" sentence = re.sub(r'(\\\\x01|\\\\x0c|\\\\x98|\\\\x99|\\\\xa6|\\\\xc2|\\\\xa0|\\\\xa9|\\\\x82|\\\\xb7)', r' ', sentence)\n",
" \n",
" # removing trade mark of specific pdf file\n",
" sentence = sentence.replace(r'LE G A SA L E M D PL O C E S', ' ') \n",
" \n",
" # striping consecutive white spaces\n",
" sentence = re.sub(r'\\s\\s+', ' ', sentence).strip()\n",
" sentence = sentence.strip()\n",
" return sentence"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clean_texts = [clean_corpus(str(doc)) for doc in corpus]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"b'PROPERTY\\t\\t\\t\\t\\t\\t- 30 -\\n\\n10.5. \\tPOLICIES OF INSURANCE\\t\\t\\t\\t\\t\\t\\t- 31 -\\n\\n10.6. \\tUNIT TRUSTS\\t\\t\\t\\t\\t\\t\\t\\t\\t- 31 -\\n\\n10.7.\\tCOMPANIES\\t\\t\\t\\t\\t\\t\\t\\t\\t- 32 -\\n\\n10.8. \\tBUSINESSES\\t\\t\\t\\t\\t\\t\\t\\t\\t- 33 -\\n\\n10.9.\\tGIFTS\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- 35 -\\n\\n10.10. \\tCHOSES-IN-ACTION\\t\\t\\t\\t\\t\\t\\t\\t- 35 -\\n\\n10.11. \\tBANKING AND FINANCE\\t\\t\\t\\t\\t\\t\\t- 35 -\\n\\n10.12. \\tLEGAL\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t' \n",
"\n",
" TION - 42 - 11.9. PROFESSIONAL AS TRUSTEE - 42 - 11.10. LIMITATION OF LIABILITY - 42 - 11.11. INDEMNITY - 42 - 12. APPOINTMENT, REMOVAL AND RESIGNATION OF TRUSTEES - 43 - 12.1. APPOINTMENT OF NEW OR ADDITIONAL TRUSTEES - 43 - 12.2. REMOVAL OF TRUSTEE - 44 - 12.3. RESIGNATION OF TRUSTEE - 45 - 12.4. \n"
]
}
],
"source": [
"print(corpus[7][3000:3300] , '\\n\\n', clean_texts[7][3000:3300])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Parts of Speech Cleaning\n",
"Spacy nlp documents were instantiated to perform parts of speech cleaning"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nlp_docs = [nlp(clean_text) for clean_text in clean_texts]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"At the trustees complete discretion, the distributions of trust income or other trust property may be made to any number of these eligible beneficiaries and in any proportion."
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentence = list(nlp_docs[23].sents)[12]\n",
"sentence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Only nouns, verbs and adjectives will be kept."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tags_to_keep = ['JJ', 'NN', 'NNP', 'NNPS', 'NNS', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Every token of the previous sentence are listed below. We can see many words that are not concepts listed, such as 'at', 'the', 'or', 'in', etc."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"At - IN - ADP\n",
"the - DT - DET\n",
"trustees - NNS - NOUN\n",
"complete - JJ - ADJ\n",
"discretion - NN - NOUN\n",
", - , - PUNCT\n",
"the - DT - DET\n",
"distributions - NNS - NOUN\n",
"of - IN - ADP\n",
"trust - NN - NOUN\n",
"income - NN - NOUN\n",
"or - CC - CCONJ\n",
"other - JJ - ADJ\n",
"trust - NN - NOUN\n",
"property - NN - NOUN\n",
"may - MD - VERB\n",
"be - VB - VERB\n",
"made - VBN - VERB\n",
"to - IN - ADP\n",
"any - DT - DET\n",
"number - NN - NOUN\n",
"of - IN - ADP\n",
"these - DT - DET\n",
"eligible - JJ - ADJ\n",
"beneficiaries - NNS - NOUN\n",
"and - CC - CCONJ\n",
"in - IN - ADP\n",
"any - DT - DET\n",
"proportion - NN - NOUN\n",
". - . - PUNCT\n"
]
}
],
"source": [
"_=[print('{} - {} - {}'.format(token, token.tag_, token.pos_)) for token in sentence]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, we selected only the words that fall into a part of speech category previously selected. We can see that this time only words that carry meaning are listed. Most of the concepts are related to the trust deeds business. For instance 'trustees', "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trustees - NNS - NOUN - trustee\n",
"complete - JJ - ADJ - complete\n",
"discretion - NN - NOUN - discretion\n",
"distributions - NNS - NOUN - distribution\n",
"trust - NN - NOUN - trust\n",
"income - NN - NOUN - income\n",
"other - JJ - ADJ - other\n",
"trust - NN - NOUN - trust\n",
"property - NN - NOUN - property\n",
"be - VB - VERB - be\n",
"made - VBN - VERB - make\n",
"number - NN - NOUN - number\n",
"eligible - JJ - ADJ - eligible\n",
"beneficiaries - NNS - NOUN - beneficiary\n",
"proportion - NN - NOUN - proportion\n"
]
}
],
"source": [
"_=[print('{} - {} - {} - {}'.format(token, token.tag_, token.pos_, token.lemma_))\n",
" for token in sentence if token.tag_ in tags_to_keep]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Besides removing filtering the token by pos tagd, the code below will also remove the remanining stop words from by using the STOPWORDS list provided by the SPACY package. This is performed by filtering the stop words with the is_stop function. To improve this filtering process, some terms found in the documents by exploration were added to the default stop list. Named entities will also be removed as they mostly refer to parties of the deeds, not to concepts related to it."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sixty; unless; enough; cannot; this; fifteen; been; otherwise; in; others; not; side; an; it; were; last; would; due; its; one; using; afterwards; five; show; around; nothing; hundred; we; where; fifty; part; few; twenty; somehow; whither; more; across; those; always; still; whereafter; anything; moreover; other; top; latter; must; ten; about; seem; something; might; formerly; anywhere; name; you; which; thereupon; throughout; go; yours; of; without; upon; latterly; anyone; however; against; and; down; my; everything; nevertheless; being; whether; who; now; namely; via; hers; various; no; our; made; own; if; front; after; hereupon; here; everywhere; his; to; ours; whenever; fax; is; a; nobody; please; together; only; so; same; seemed; re; do; full; although; never; put; himself; yet; back; say; though; used; many; had; therein; between; again; within; he; can; almost; less; somewhere; four; else; another; ourselves; seeming; have; three; these; none; get; all; along; could; once; during; sa m ple; nine; are; nowhere; first; amount; becoming; for; the; myself; she; move; too; thereafter; whereby; eight; became; by; just; least; thus; whereupon; whose; or; most; each; has; into; whole; over; when; inc; at; whom; yourself; either; two; noone; ever; on; much; why; me; next; wherever; will; already; really; beside; neither; how; m; SA; amongst; yourselves; ca; give; some; us; hereafter; your; whence; towards; done; since; indeed; until; seems; several; six; onto; below; even; mostly; further; hereby; thence; they; herein; third; eleven; everyone; them; twelve; sometime; thereby; see; beyond; except; etc; anyway; besides; through; often; that; someone; behind; keep; be; perhaps; serious; herself; while; forty; beforehand; up; former; rather; but; i; under; meanwhile; doing; should; than; itself; becomes; very; every; whatever; regarding; bottom; was; may; as; take; therefore; quite; wherein; well; before; both; alone; call; e; off; empty; also; her; mine; any; hence; what; elsewhere; out; above; with; from; thru; sometimes; among; anyhow; him; did; become; because; their; there; whereas; does; nor; make; themselves; per; then; toward; am; such; whoever\n"
]
}
],
"source": [
"spacy.en.STOP_WORDS.update(['SA', 'fax', 'sa m ple', 'm', 'e'])\n",
"print (\"; \".join(spacy.en.STOP_WORDS))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will also take advantage of the named entity recognition capabilities of the Spacy API to remove entities that cannot contribute to the finding of meaningful concepts in the deeds corpus. The only entity left in the corpus was the 'ORG', that stands for Companies, agencies, institutions, etc. (https://spacy.io/docs/usage/entity-recognition)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"entities_to_remove = ['PERSON', 'GPE', 'LOC', 'NORP', 'FACILITY', 'PRODUCT', 'EVENT', 'WORK_OF_ART',\n",
" 'LANGUAGE', 'DATE', 'TIME', 'PERCENT', 'MONEY', 'QUANTITY', 'ORDINAL', 'CARDINAL']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The filtered text will receive only the lemma of the nouns, verbs and adjectives. In this way, the lemmatisation is already performed and variations of a certain word will be returned just as their common lemma. Furthermore, the lemmas in the spacy API are set in lowercase. Thus, the output text will be prepared to be processed into a vector space."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filtered_texts = []\n",
"for doc in nlp_docs:\n",
" filtered_text = ''\n",
" for sentence in doc.sents:\n",
" sent_filt_text = ' '.join([token.lemma_ \n",
" for token in sentence if (token.tag_ in tags_to_keep \n",
" and not token.is_stop\n",
" and not token.ent_type_ in entities_to_remove\n",
" )])\n",
" filtered_text = filtered_text + ' ' + sent_filt_text\n",
" filtered_texts.append(filtered_text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform a second regular expression cleaning to remove dirties found by parts of speech when it tokenised the text."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filtered_texts = [clean_corpus(str(doc)) for doc in filtered_texts]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.1. Definitions \"Appointor\" shall mean the person or persons (if any) named or described as such in the Schedule and any person or persons who succeed them in that position in accordance with the provisions of this Deed alive or in existence at any time or where at any time no such person or persons are alive or in existence the legal personal representatives of the last surviving one of them; \"Beneficiary\" means a Specified Beneficiary or a General Beneficiary; \"Business Day\" means a day that is not a Saturday, Sunday or any other day which is a public holiday or a bank holiday in the place where the act is to be performed or payment is to be made; \"Capital Gains\" means that part of the capital receipts of the Trust Fund which are liable to be treated as assessable income for the purposes of the Income Tax Law \n",
"\n",
" definition appointor mean person person name describe schedule person person succe position accordance provision deed alive existence time time person person alive existence legal personal representative survive mean specified beneficiary general business day mean day public holiday bank holiday place act perform payment mean capital receipt trust fund liable treat assessable income purpose income tax law mean\n"
]
}
],
"source": [
"print(clean_texts[7][5291:6114], '\\n\\n', filtered_texts[7][2412:2825])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Building the models to find correlated concepts"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"#modules to build the topic extracting models\n",
"from gensim import corpora, models\n",
"import gensim\n",
"import pyLDAvis.gensim\n",
"pyLDAvis.enable_notebook()\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2767 different terms in the corpus\n"
]
}
],
"source": [
"#creating the dictionary\n",
"dictionary = corpora.Dictionary([\" \".join(filtered_texts).split()]) \n",
"print('{} different terms in the corpus'.format(len(dictionary)))\n",
"#creating the bag of words object\n",
"bow_corpus = [dictionary.doc2bow(text.split()) for text in filtered_texts]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tfidf_model = models.TfidfModel(bow_corpus) # creating the tf-idf model\n",
"tfidf_corpus = tfidf_model[bow_corpus]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Building the LDA models."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/EduardoAndrade/anaconda/lib/python3.6/site-packages/gensim/models/ldamodel.py:529: RuntimeWarning: overflow encountered in exp2\n",
" (perwordbound, np.exp2(-perwordbound), len(chunk), corpus_words))\n"
]
}
],
"source": [
"total_topics = 50\n",
"lda_model_tfidf = models.LdaModel(corpus=tfidf_corpus, id2word=dictionary, num_topics=total_topics, \n",
" passes=1, random_state=47)\n",
"lda_model_bow = models.LdaModel(corpus=bow_corpus, id2word=dictionary, num_topics=total_topics,\n",
" passes=1, random_state=47)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/EduardoAndrade/anaconda/lib/python3.6/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
" topic_term_dists = topic_term_dists.ix[topic_order]\n"
]
},
{
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"\n",
"<div id=\"ldavis_el1207358195495205299208295\"></div>\n",
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\"|signature\", \"|signature\", \"|signed\", \"|signed\", \"|signed\", \"|trustee\", \"|trustee\", \"~\", \"~\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [4, 41, 45, 28, 15, 23, 11, 48, 38, 37, 21, 46, 8, 40, 29, 24, 32, 14, 13, 16, 50, 12, 10, 9, 18, 7, 6, 5, 3, 2, 17, 25, 19, 20, 47, 44, 43, 42, 39, 36, 35, 34, 33, 31, 30, 27, 26, 49, 22, 1]};\n",
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" LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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"text/plain": [
"PreparedData(topic_coordinates= Freq cluster topics x y\n",
"topic \n",
"3 32.051210 1 1 -0.009426 0.000220\n",
"40 14.432954 1 2 -0.016656 -0.000772\n",
"44 11.951673 1 3 -0.009170 -0.006819\n",
"27 11.709898 1 4 -0.006163 -0.000390\n",
"14 8.429031 1 5 -0.008492 -0.004052\n",
"22 8.123675 1 6 -0.019578 0.002358\n",
"10 7.091608 1 7 -0.017685 0.011123\n",
"47 2.074981 1 8 -0.017349 -0.003336\n",
"37 1.377237 1 9 -0.015923 -0.000776\n",
"36 1.224570 1 10 -0.018588 0.000969\n",
"20 0.724880 1 11 -0.010825 -0.000722\n",
"45 0.352858 1 12 -0.004208 -0.003611\n",
"7 0.177558 1 13 0.000438 0.007935\n",
"39 0.116154 1 14 -0.008278 -0.001428\n",
"28 0.115098 1 15 -0.014198 -0.000870\n",
"23 0.028988 1 16 -0.014439 0.007439\n",
"31 0.000584 1 17 -0.003358 -0.007850\n",
"13 0.000516 1 18 -0.005742 -0.003039\n",
"12 0.000516 1 19 -0.004047 -0.001036\n",
"15 0.000516 1 20 0.004605 -0.000681\n",
"49 0.000516 1 21 -0.002179 -0.003746\n",
"11 0.000516 1 22 0.054969 -0.000391\n",
"9 0.000516 1 23 0.005431 -0.001062\n",
"8 0.000516 1 24 -0.003530 -0.001900\n",
"17 0.000516 1 25 0.047555 0.005476\n",
"6 0.000516 1 26 -0.002678 -0.001012\n",
"5 0.000516 1 27 0.001971 -0.004411\n",
"4 0.000516 1 28 0.012761 -0.007691\n",
"2 0.000516 1 29 0.003698 -0.001087\n",
"1 0.000516 1 30 0.013436 -0.001733\n",
"16 0.000516 1 31 0.006371 0.010257\n",
"24 0.000516 1 32 0.018824 -0.011184\n",
"18 0.000516 1 33 -0.001318 0.000892\n",
"19 0.000516 1 34 -0.001137 -0.000184\n",
"46 0.000516 1 35 -0.007494 0.005305\n",
"43 0.000516 1 36 0.009254 -0.000776\n",
"42 0.000516 1 37 -0.001399 -0.005656\n",
"41 0.000516 1 38 0.001313 0.010848\n",
"38 0.000516 1 39 0.006333 0.005802\n",
"35 0.000516 1 40 -0.003161 0.003768\n",
"34 0.000516 1 41 0.015113 -0.007060\n",
"33 0.000516 1 42 0.023363 0.011138\n",
"32 0.000516 1 43 0.002210 -0.002436\n",
"30 0.000516 1 44 0.006277 0.002179\n",
"29 0.000516 1 45 -0.009993 0.001917\n",
"26 0.000516 1 46 -0.008734 0.003355\n",
"25 0.000516 1 47 0.004174 0.000216\n",
"48 0.000516 1 48 0.003151 0.000270\n",
"21 0.000516 1 49 -0.001408 -0.005583\n",
"0 0.000516 1 50 0.005912 -0.000174, topic_info= Category Freq Term Total loglift logprob\n",
"term \n",
"17 Default 6319.000000 trustee 6319.000000 30.0000 30.0000\n",
"4 Default 3675.000000 trust 3675.000000 29.0000 29.0000\n",
"27 Default 2360.000000 fund 2360.000000 28.0000 28.0000\n",
"49 Default 1971.000000 person 1971.000000 27.0000 27.0000\n",
"61 Default 1688.000000 income 1688.000000 26.0000 26.0000\n",
"24 Default 1344.000000 power 1344.000000 25.0000 25.0000\n",
"140 Default 1285.000000 time 1285.000000 24.0000 24.0000\n",
"62 Default 1255.000000 beneficiary 1255.000000 23.0000 23.0000\n",
"5 Default 1184.000000 deed 1184.000000 22.0000 22.0000\n",
"108 Default 1003.000000 property 1003.000000 21.0000 21.0000\n",
"76 Default 766.000000 clause 766.000000 20.0000 20.0000\n",
"60 Default 763.000000 act 763.000000 19.0000 19.0000\n",
"19 Default 796.000000 pay 796.000000 18.0000 18.0000\n",
"395 Default 722.000000 company 722.000000 17.0000 17.0000\n",
"196 Default 758.000000 exercise 758.000000 16.0000 16.0000\n",
"48 Default 708.000000 include 708.000000 15.0000 15.0000\n",
"46 Default 680.000000 appointor 680.000000 14.0000 14.0000\n",
"83 Default 647.000000 capital 647.000000 13.0000 13.0000\n",
"129 Default 605.000000 discretion 605.000000 12.0000 12.0000\n",
"82 Default 608.000000 share 608.000000 11.0000 11.0000\n",
"167 Default 590.000000 money 590.000000 10.0000 10.0000\n",
"239 Default 569.000000 interest 569.000000 9.0000 9.0000\n",
"36 Default 550.000000 account 550.000000 8.0000 8.0000\n",
"37 Default 477.000000 period 477.000000 7.0000 7.0000\n",
"38 Default 464.000000 mean 464.000000 6.0000 6.0000\n",
"21 Default 437.000000 hold 437.000000 5.0000 5.0000\n",
"297 Default 446.000000 distribution 446.000000 4.0000 4.0000\n",
"252 Default 420.000000 fit 420.000000 3.0000 3.0000\n",
"244 Default 412.000000 law 412.000000 2.0000 2.0000\n",
"22 Default 420.000000 apply 420.000000 1.0000 1.0000\n",
"... ... ... ... ... ... ...\n",
"82 Topic50 0.003667 share 608.232894 0.1547 -4.9151\n",
"366 Topic50 0.002543 director 387.766379 0.2390 -5.2810\n",
"60 Topic50 0.004345 act 763.686611 0.0970 -4.7452\n",
"49 Topic50 0.009089 person 1971.122189 -0.1132 -4.0073\n",
"775 Topic50 0.000649 contract 74.715862 0.5196 -6.6470\n",
"61 Topic50 0.007730 income 1688.545112 -0.1205 -4.1692\n",
"76 Topic50 0.004134 clause 766.811076 0.0431 -4.7951\n",
"52 Topic50 0.000926 sole 117.300054 0.4241 -6.2915\n",
"41 Topic50 0.002228 date 357.214169 0.1888 -5.4132\n",
"81 Topic50 0.001703 corporation 254.292663 0.2599 -5.6820\n",
"83 Topic50 0.003553 capital 647.905013 0.0602 -4.9465\n",
"17 Topic50 0.020776 trustee 6319.813300 -0.4516 -3.1805\n",
"196 Topic50 0.003952 exercise 758.019644 0.0095 -4.8402\n",
"48 Topic50 0.003732 include 708.533504 0.0198 -4.8973\n",
"235 Topic50 0.001882 right 299.642298 0.1957 -5.5821\n",
"107 Topic50 0.002195 respect 367.175735 0.1465 -5.4281\n",
"195 Topic50 0.001998 absolute 328.855014 0.1626 -5.5222\n",
"5 Topic50 0.005224 deed 1184.968915 -0.1582 -4.5611\n",
"21 Topic50 0.002454 hold 437.969102 0.0816 -5.3167\n",
"251 Topic50 0.002244 think 390.052460 0.1082 -5.4059\n",
"377 Topic50 0.002142 security 369.818336 0.1149 -5.4525\n",
"36 Topic50 0.002838 account 550.196017 -0.0010 -5.1711\n",
"46 Topic50 0.003268 appointor 680.100780 -0.0721 -5.0303\n",
"19 Topic50 0.003534 pay 796.109920 -0.1511 -4.9518\n",
"165 Topic50 0.002125 appoint 368.929744 0.1091 -5.4607\n",
"62 Topic50 0.004217 beneficiary 1255.152163 -0.4298 -4.7751\n",
"167 Topic50 0.002730 money 590.216663 -0.1103 -5.2101\n",
"108 Topic50 0.003099 property 1003.550147 -0.5140 -5.0831\n",
"38 Topic50 0.002230 mean 464.223178 -0.0723 -5.4123\n",
"239 Topic50 0.002260 interest 569.180759 -0.2626 -5.3987\n",
"\n",
"[4051 rows x 6 columns], token_table= Topic Freq Term\n",
"term \n",
"2021 1 0.415066 &\n",
"2089 1 0.695781 2f\n",
"1459 1 0.241439 aaa\n",
"1459 4 0.241439 aaa\n",
"1507 1 0.176470 able\n",
"1507 2 0.176470 able\n",
"1507 3 0.176470 able\n",
"1507 4 0.176470 able\n",
"1507 6 0.176470 able\n",
"195 1 0.334494 absolute\n",
"195 2 0.097307 absolute\n",
"195 3 0.158124 absolute\n",
"195 4 0.133798 absolute\n",
"195 5 0.079062 absolute\n",
"195 6 0.082103 absolute\n",
"195 7 0.057776 absolute\n",
"195 8 0.015204 absolute\n",
"195 9 0.012163 absolute\n",
"195 10 0.012163 absolute\n",
"195 11 0.009123 absolute\n",
"195 12 0.003041 absolute\n",
"195 13 0.003041 absolute\n",
"168 1 0.300794 accept\n",
"168 2 0.150397 accept\n",
"168 3 0.133686 accept\n",
"168 4 0.100265 accept\n",
"168 5 0.091909 accept\n",
"168 6 0.083554 accept\n",
"168 7 0.083554 accept\n",
"168 8 0.025066 accept\n",
"... ... ... ...\n",
"2012 1 0.471568 |power\n",
"2012 4 0.235784 |power\n",
"2012 7 0.235784 |power\n",
"2009 1 0.356390 |presence\n",
"2009 4 0.178195 |presence\n",
"2009 7 0.178195 |presence\n",
"2013 1 0.342004 |print\n",
"2013 2 0.085501 |print\n",
"2013 4 0.171002 |print\n",
"2013 5 0.085501 |print\n",
"2013 6 0.085501 |print\n",
"2013 7 0.171002 |print\n",
"2008 1 0.602487 |settlor\n",
"1444 1 0.433829 |sign\n",
"1444 2 0.108457 |sign\n",
"1444 4 0.216914 |sign\n",
"1444 7 0.108457 |sign\n",
"2010 1 0.478809 |signature\n",
"2010 2 0.087056 |signature\n",
"2010 4 0.174112 |signature\n",
"2010 5 0.043528 |signature\n",
"2010 6 0.043528 |signature\n",
"2010 7 0.174112 |signature\n",
"2007 1 0.542897 |signed\n",
"2007 4 0.135724 |signed\n",
"2007 7 0.135724 |signed\n",
"2015 1 0.317892 |trustee\n",
"2015 4 0.317892 |trustee\n",
"2702 1 0.319143 ~\n",
"2702 3 0.319143 ~\n",
"\n",
"[4859 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[4, 41, 45, 28, 15, 23, 11, 48, 38, 37, 21, 46, 8, 40, 29, 24, 32, 14, 13, 16, 50, 12, 10, 9, 18, 7, 6, 5, 3, 2, 17, 25, 19, 20, 47, 44, 43, 42, 39, 36, 35, 34, 33, 31, 30, 27, 26, 49, 22, 1])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pyLDAvis.gensim.prepare(lda_model_bow, bow_corpus, dictionary)\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculating the topics for documents 03, 12 and 17"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"vec_bow_17 = dictionary.doc2bow(filtered_texts[17].split())\n",
"vec_bow_03 = dictionary.doc2bow(filtered_texts[3].split())\n",
"vec_bow_12 = dictionary.doc2bow(filtered_texts[12].split())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Viewing the topics of the selected documents"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"document 03 topics: [(3, 0.075345944039359194), (27, 0.40886112389944279), (39, 0.018643289660613249), (40, 0.02759284971293401), (44, 0.45194924764671079)]\n",
"document 12 topics: [(10, 0.16041004212835228), (22, 0.44745773180268861), (36, 0.034127220990991462), (37, 0.020306193989949425), (40, 0.30730365022374589), (47, 0.015509030752539409)]\n",
"document 17 topics: [(3, 0.43117910486800543), (14, 0.043080651275866973), (27, 0.33344959275418595), (40, 0.024718801032967929), (44, 0.16250302721191706)]\n"
]
}
],
"source": [
"vec_lda_topics_17 = lda_model_bow[vec_bow_17]\n",
"vec_lda_topics_03 = lda_model_bow[vec_bow_03]\n",
"vec_lda_topics_12 = lda_model_bow[vec_bow_12]\n",
"print ('document 03 topics: ', vec_lda_topics_03)\n",
"print ('document 12 topics: ', vec_lda_topics_12)\n",
"print ('document 17 topics: ', vec_lda_topics_17)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df1 = pd.DataFrame(vec_lda_topics_03, columns=['topic', 'contrib'])\n",
"df1['doc'] = 'doc_03'\n",
"df2 = pd.DataFrame(vec_lda_topics_12, columns=['topic', 'contrib'])\n",
"df2['doc'] = 'doc_12'\n",
"df3 = pd.DataFrame(vec_lda_topics_17, columns=['topic', 'contrib'])\n",
"df3['doc'] = 'doc_17'"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
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IV8OexVBx8zlhZecvcOGjdVRmXMbAzhZTHx8Kj0dw6rU3cH96Eo6jRzb5Itxu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\nIAiCIAidIJIoQRAEQRCETvg/eADP//4tmVcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15ae54550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=[10,3])\n",
"plt.plot(df1.topic, df1.contrib)\n",
"plt.plot(df2.topic, df2.contrib)\n",
"plt.plot(df3.topic, df3.contrib)\n",
"plt.legend(['Doc_03', 'Doc_12', 'Doc_17'], loc='upper left')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculating the simmilarity between the documents\n",
"Documents 3 and 17 are closer to one another than 3 is to 12 or 12 is to 17."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"simmilarity_03_12 = gensim.matutils.cossim(vec_lda_topics_03, vec_lda_topics_12)\n",
"simmilarity_03_17 = gensim.matutils.cossim(vec_lda_topics_03, vec_lda_topics_17)\n",
"simmilarity_12_17 = gensim.matutils.cossim(vec_lda_topics_12, vec_lda_topics_17)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"similarity between docs 03 and 12 0.0242902625579\n",
"similarity between docs 03 and 17 0.691911431814\n",
"similarity between docs 12 and 17 0.0234388394818\n"
]
}
],
"source": [
"print('similarity between docs 03 and 12', simmilarity_03_12)\n",
"print('similarity between docs 03 and 17', simmilarity_03_17)\n",
"print('similarity between docs 12 and 17', simmilarity_12_17)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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