In [2]:

    

import xmltodict
import glob
from nltk.tokenize import RegexpTokenizer
import nltk
from stop_words import get_stop_words
from nltk.stem.porter import PorterStemmer
from nltk.stem.snowball import SnowballStemmer
from gensim import corpora, models
import gensim
from time import time
from sklearn.externals import joblib
import gzip
import string
from nltk.stem.wordnet import WordNetLemmatizer
from nltk import word_tokenize

import pyLDAvis
import pyLDAvis.gensim


    

In [18]:

    

exclude = list(string.punctuation) 
exclude.extend(['\'s', '\'\'', '``', '–', '‘a', '--', '...'])
# print(exclude)
lemma = WordNetLemmatizer()
# create English stop words list
en_stop = get_stop_words('en')
# Create p_stemmer of class PorterStemmer
p_stemmer = PorterStemmer()
sb_stemmer = SnowballStemmer("english")

def clean(doc):
    stop_free = [i for i in doc if i not in en_stop]
    punc_free = [ch for ch in stop_free if ch not in exclude]
    punc_free = [ch for ch in punc_free if len(ch) > 1]
    normalized = [lemma.lemmatize(word) for word in punc_free]
    # stem tokens 
    # stemmed_tokens = [sb_stemmer.stem(i) for i in normalized]
    return normalized


    

In [19]:

    

# NLKT 
nltk.download("wordnet")


    

    




[nltk_data] Downloading package wordnet to /home/sonic/nltk_data...
[nltk_data]   Unzipping corpora/wordnet.zip.






    
Out[19]:





True

In [5]:

    

# <Document>
# <Id>en_2014-01-12_99ed1bbd8128fe754e1d37ef5a13384f59a2b49</Id>
# <SourceName>Arutz Sheva</SourceName>
# <CaptureDateTime>01 Dec 2014 00:00:00</CaptureDateTime>
# <PublicationDateTime>01 Dec 2014 00:00:00</PublicationDateTime>
# <Title>Atty-General to Seek Controversial Mayor’s Ouster</Title>
# <TranslatedTitle/>
# <SourceCoverage>ISRAEL</SourceCoverage>
# <Url>http://www.israelnationalnews.com/News/News.aspx/176185</Url>
# <Encoding>UTF-8</Encoding>
# <Language>English</Language><SourceType/>
# <RobotName>israelnat4048</RobotName>
# <Text>Document Test</Text>
# </Document>


start = time()
corpus = []

# path = "data/Karbala/en_2014-01-12_a1e0ca1a503d6595d49e02207069a524d3d42.xml"
path = "/home/sonic/sonic/EOS_DATA/XML_Export_6-8-2015/ContainingTerms/English/*/*.xml"
for fname in glob.glob(path):
    with open(fname, 'r', encoding='utf-8') as fd:
        # print(fname)
        try:
            doc = xmltodict.parse(fd.read())
            # print(doc['Document']['Id'])
            # print(doc['Document']['Text'])
            tokens = word_tokenize(doc['Document']['Text'].lower())
            # print(tokens)
            # add tokens to list
            corpus.append(clean(tokens))
        except Exception as e: 
            print(e)

print('corpus size:', len(corpus))

tokens_rm_stopwords = open('data/eos_tokens_stopwords.txt', 'w')
for item in corpus:
    tokens_rm_stopwords.write("%s\n" % item)

print('Cell took %.2f seconds to run.' % (time() - start))


    

    




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---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-5-46e6038a7062> in <module>()
     27             # print(doc['Document']['Id'])
     28             # print(doc['Document']['Text'])
---> 29             tokens = word_tokenize(doc['Document']['Text'].lower())
     30             # print(tokens)
     31             # add tokens to list

/usr/local/lib/python3.5/dist-packages/nltk/tokenize/__init__.py in word_tokenize(text, language)
    107     :param language: the model name in the Punkt corpus
    108     """
--> 109     return [token for sent in sent_tokenize(text, language)
    110             for token in _treebank_word_tokenize(sent)]
    111 

/usr/local/lib/python3.5/dist-packages/nltk/tokenize/__init__.py in <listcomp>(.0)
    108     """
    109     return [token for sent in sent_tokenize(text, language)
--> 110             for token in _treebank_word_tokenize(sent)]
    111 

/usr/local/lib/python3.5/dist-packages/nltk/tokenize/treebank.py in tokenize(self, text)
     96     def tokenize(self, text):
     97         for regexp, substitution in self.STARTING_QUOTES:
---> 98             text = regexp.sub(substitution, text)
     99 
    100         for regexp, substitution in self.PUNCTUATION:

/usr/lib/python3.5/re.py in _subx(pattern, template)
    321     return sre_parse.expand_template(template, match)
    322 
--> 323 def _subx(pattern, template):
    324     # internal: pattern.sub/subn implementation helper
    325     template = _compile_repl(template, pattern)

KeyboardInterrupt: 

In [4]:

    

print(corpus[0])


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-4-59555ee8575a> in <module>()
----> 1 print(corpus[0])

NameError: name 'corpus' is not defined

In [22]:

    

# turn our tokenized documents into a id <-> term dictionary
dictionary = corpora.Dictionary(corpus)
dictionary.save_as_text('data/dictionary_EOS.txt')

# convert tokenized documents into a document-term matrix
corpus = [dictionary.doc2bow(text) for text in corpus]
corpora.MmCorpus.serialize('data/nostopwords_corpus_EOS.mm', corpus)


    

In [3]:

    

# generate LDA model
# ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=2, id2word=dictionary, passes=20)
start = time()
ldamodel = gensim.models.ldamulticore.LdaMulticore(corpus, id2word=dictionary, num_topics=20, chunksize=1000, passes=20, workers=4)
joblib.dump(ldamodel, 'data/ldamodel_20_EOS.pkl')
print('Cell took %.2f seconds to run.' % (time() - start))


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-3-28f76bdb47c5> in <module>()
      2 # ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=2, id2word=dictionary, passes=20)
      3 start = time()
----> 4 ldamodel = gensim.models.ldamulticore.LdaMulticore(corpus, id2word=dictionary, num_topics=20, chunksize=1000, passes=20, workers=4)
      5 joblib.dump(ldamodel, 'data/ldamodel_20_EOS.pkl')
      6 print('Cell took %.2f seconds to run.' % (time() - start))

NameError: name 'corpus' is not defined

In [ ]:

    




    

In [57]:

    

corpus = gensim.corpora.MmCorpus('data/nostopwords_corpus_EOS.mm')
lda = joblib.load('data/ldamodel_20_EOS.pkl')

(lda.print_topics(num_topics=20, num_words=8))


    

    
Out[57]:





[(0,
  '0.015*"will" + 0.007*"company" + 0.006*"al" + 0.006*"development" + 0.006*"dubai" + 0.005*"project" + 0.005*"service" + 0.005*"business"'),
 (1,
  '0.017*"2014" + 0.014*"dec" + 0.013*"alert" + 0.012*"reply" + 0.012*"moderator" + 0.008*"n\'t" + 0.008*"will" + 0.007*"de"'),
 (2,
  '0.009*"court" + 0.007*"said" + 0.004*"case" + 0.004*"will" + 0.004*"police" + 0.004*"hospital" + 0.004*"new" + 0.004*"district"'),
 (3,
  '0.018*"said" + 0.015*"state" + 0.013*"islamic" + 0.013*"syria" + 0.012*"iraq" + 0.008*"group" + 0.007*"militant" + 0.006*"iraqi"'),
 (4,
  '0.020*"report" + 0.019*"cia" + 0.012*"said" + 0.010*"interrogation" + 0.008*"torture" + 0.007*"senate" + 0.007*"intelligence" + 0.006*"u"'),
 (5,
  '0.009*"oil" + 0.007*"said" + 0.007*"year" + 0.007*"price" + 0.006*"market" + 0.006*"point" + 0.006*"million" + 0.005*"game"'),
 (6,
  '0.024*"في" + 0.016*"من" + 0.010*"على" + 0.005*"أن" + 0.005*"bangladesh" + 0.004*"إلى" + 0.004*"التي" + 0.004*"الى"'),
 (7,
  '0.007*"patient" + 0.007*"time" + 0.006*"study" + 0.005*"director" + 0.005*"disease" + 0.005*"research" + 0.005*"film" + 0.004*"certificate"'),
 (8,
  '0.013*"sa" + 0.010*"ang" + 0.007*"na" + 0.006*"ng" + 0.004*"di" + 0.004*"mga" + 0.004*"wine" + 0.003*"ni"'),
 (9,
  '0.031*"der" + 0.031*"die" + 0.018*"und" + 0.011*"da" + 0.010*"den" + 0.009*"von" + 0.008*"zu" + 0.008*"mit"'),
 (10,
  '0.016*"al" + 0.013*"said" + 0.011*"attack" + 0.008*"kenya" + 0.008*"2014" + 0.007*"police" + 0.007*"killed" + 0.007*"bin"'),
 (11,
  '0.028*"agency" + 0.019*"cup" + 0.018*"calgary" + 0.016*"grey" + 0.015*"sun/qmi" + 0.015*"30" + 0.015*"stampeders" + 0.014*"vancouver"'),
 (12,
  '0.017*"در" + 0.016*"..." + 0.012*"به" + 0.009*"از" + 0.009*"که" + 0.007*"این" + 0.007*"با" + 0.006*"را"'),
 (13,
  '0.012*"said" + 0.006*"government" + 0.005*"will" + 0.005*"people" + 0.004*"country" + 0.004*"year" + 0.004*"also" + 0.003*"one"'),
 (14,
  '0.012*"egypt" + 0.010*"said" + 0.008*"russia" + 0.007*"egyptian" + 0.007*"mubarak" + 0.006*"year" + 0.006*"president" + 0.005*"court"'),
 (15,
  '0.019*"said" + 0.010*"hostage" + 0.009*"yemen" + 0.007*"u" + 0.007*"somers" + 0.006*"video" + 0.005*"korkie" + 0.005*"south"'),
 (16,
  '0.031*"police" + 0.019*"new" + 0.019*"york" + 0.017*"garner" + 0.017*"officer" + 0.014*"--" + 0.013*"grand" + 0.012*"jury"'),
 (17,
  '0.071*"de" + 0.033*"la" + 0.023*"în" + 0.023*"şi" + 0.012*"cu" + 0.011*"din" + 0.010*"care" + 0.009*"se"'),
 (18,
  '0.019*"turkish" + 0.016*"turkey" + 0.016*"istanbul" + 0.008*"protest" + 0.006*"world" + 0.006*"photo" + 0.005*"police" + 0.005*"year"'),
 (19,
  '0.009*"uranium" + 0.008*"question" + 0.008*"afghanistan" + 0.006*"will" + 0.006*"u" + 0.006*"pakistan" + 0.006*"said" + 0.006*"exploration"')]

In [58]:

    

lda_vis = pyLDAvis.gensim.prepare(lda, corpus, dictionary)
pyLDAvis.display(lda_vis)


    

    




/usr/local/lib/python3.5/dist-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
  topic_term_dists = topic_term_dists.ix[topic_order]






    
Out[58]:

In [ ]: