def preprocess_corpora(corpora, stopwords, allowed_pos, max_doc=float('inf'), no_above=0.5, no_below=1, keep_n=None):
"""
:rtype : gensim.corpora.dictionary.Dictionary
:param corpora:
:param stopwords:
:param allowed_pos:
:param max_doc:
:return:
"""
logging.info('Lemmatizing the corpora...')
count = 0
corpus_num = len(corpora)
processed_corpora = []
corpus_id2orig_id = []
for index, corpus in corpora.items():
count += 1
if count > max_doc:
break
if corpus is None: # skip if corpus is None
continue
print '\r', count, '/', corpus_num,
cleaned_corpus = clean_text(corpus) # delete irrelevant characters
corpus = []
tokens = lemmatize(content=cleaned_corpus, allowed_tags=allowed_pos)
for token in tokens:
word, pos = token.split('/')
corpus.append(word)
# convert compound word into one token
corpus = convert_compound(corpus)
# filter stop words, long words, and non-english words
corpus = [w for w in corpus if not w in stopwords and 2 <= len(w) <= 15 and w.islower()]
processed_corpora.append(corpus)
corpus_id2orig_id.append(index)
print '\n'
logging.info('Creating dictionary and corpus...')
dictionary = Dictionary(processed_corpora)
dictionary.corpus_id2orig_id = corpus_id2orig_id
logging.info('Filtering unimportant terms...')
dictionary.filter_extremes(no_below=no_below, no_above=no_above, keep_n=keep_n)
dictionary.compactify()
logging.info('Generating corpus...')
dictionary.corpus = [dictionary.doc2bow(corpus) for corpus in processed_corpora]
dictionary.id2token = revdict(dictionary.token2id)
return dictionary
doc_num = len(documents)
new_documents = []
titles = []
froms = []
dates = []
for index, document in documents.items():
count += 1
if count > max_doc:
break
print '\r', count, '/', doc_num,
text = document['text'] + (' ' + index) * title_weight # incorporate title information
from_name = document['from']
date = document['date']
cleaned = clean_text(text) # delete irrelevant characters
document = []
tokens = lemmatize(content=cleaned, allowed_tags=allowed_pos) # lemmatize
for token in tokens:
word, pos = token.split('/')
document.append(word)
# convert compound word into one token
document = convert_compound(document)
# filter stop words, long words, and non-english words
document = [w for w in document if not w in stop_words and 2 <= len(w) <= 15 and w.islower()]
new_documents.append(document)
titles.append(index)
access_token_secret='c0S9NKtzXU9dyRrsVqura0EIzXFBIWro3rvJBkrCdSyJe')
print api.VerifyCredentials()
# wikis = unpickle('data/others/wikis.pkl')
# keywords = wikis.keys()
# keywords = get_keywords(model_path='data/model/tweets_100.lda')
keywords = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u',
'v', 'w', 'x', 'y', 'z'] * 100
tweets = []
for i, keyword in enumerate(keywords):
try:
print "{}th done".format(i)
res = api.GetSearch(term=keyword, lang='english', count=25)
for tweet in res:
tweets.append(unicode(tweet.AsDict()['text']).replace('\n', ''))
except Exception as e:
print e
if i % 150 == 0 and i > 0:
f = open(file_name, 'ab')
csvWriter = csv.writer(f)
rx = re.compile('\W+')
for tweet in tweets:
tweet = clean_text(tweet)
tweet = rx.sub(' ', tweet).strip()
csvWriter.writerow([tweet])
tweets = []
f.close()
sleep(60 * 15) # avoid exceeding the API limit