def execute():
print 'Started at ' + get_time() + '... ',
start_timing()
client.drop_database(TOPIC_TWEETS_DB_NAME)
results = entity_results_coll.find(limit=NUMBER_OF_TOP_ENTITIES, no_cursor_timeout=True) \
.sort([(VALUE + '.' + COUNT, DESCENDING)])
for result in results:
tweets = []
text = []
lower_entity = result[LOWER_ENTITY]
entities = result[VALUE][PSEUDONYMS]
entity_pseudos[lower_entity] = entities
max_tweets = 0
for entity in entities:
c = 0
for tweet in raw_collection.find({ENTITIES: entity}):
c += 1
tweets.append(tweet)
text.append(tweet[TWEET])
if c > max_tweets:
actual_entity[lower_entity] = entity
max_tweets = c
text = clean(text)
topic_id = get_topic_for_entity(text, tweets)
entity_topic[lower_entity] = topic_id
save_to_collection()
save_model_data()
print 'Finished'
stop_timing()
def execute():
start_timing()
print 'Starting Pre-processing for LDA...',
documents = get_documents()
tokenized_documents = clean(documents)
dictionary = corpora.Dictionary([doc for doc in tokenized_documents])
dictionary.compactify()
dictionary.save(DICTIONARY_PATH)
corpus = [dictionary.doc2bow(doc) for doc in tokenized_documents]
corpora.MmCorpus.serialize(CORPUS_PATH, corpus)
print 'Finished'
stop_timing()
client.close()
def execute():
start_timing()
print 'Starting Pre-processing for LDA...',
documents = get_documents()
tokenized_documents = clean(documents)
dictionary = corpora.Dictionary([doc for doc in tokenized_documents])
dictionary.compactify()
dictionary.save(DICTIONARY_PATH)
corpus = [dictionary.doc2bow(doc) for doc in tokenized_documents]
corpora.MmCorpus.serialize(CORPUS_PATH, corpus)
print 'Finished'
stop_timing()
client.close()
