def __init__(self):
self.splitter = Splitter()
self.postagger = POSTagger()
self.dicttagger = DictionaryTagger([
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/positive.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/negative.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/morePositive.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/moreNegative.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/invert.yml'
])
def main(keys='keys.ini',
raw_tweets_file='twitter_data.txt',
no_tweets=1000,
tracked_words_file='tracks.csv',
formatted_tweets_file='formatted_tweets.txt',
dictionaries=['misoginy_dictionary.yml', 'curses_dictionary.yml']):
"""Perform an analyisis to find sexist and rude words in tweets
This module employs every other module to perform a full analysis on data
retrieved from the Twitter stream. First a TwitterMiner retrieves data and
dumps it, then a TweetFormatter parses the data into a list of tweets that
are lists of words. Then it uses the spaghetti tagger to POStag every word,
yielding a list of tweets that are lists with elements with the form (word,
[tags]). A DictionaryTagger adds our custom tags to the [tags] list. Finally
a TagCounter perform a count of every tag found in tweets. This program
prints the number of coincidences of our custom tags.
"""
miner = TwitterMiner(keys, raw_tweets_file, no_tweets)
miner.mine(tracked_words_file)
formatter = TweetFormatter(raw_tweets_file)
tweets = formatter.convert2json()
tweets = formatter.convert2text(tweets, formatted_tweets_file)
tweets = formatter.clean_tweets(tweets)
tweets = [tweet.split() for tweet in tweets]
tagger = DictionaryTagger(dictionaries)
postagged_sents = spgt.pos_tag_sents(tweets)
tagged_sents = tagger.tag(postagged_sents)
counter = TagCounter(tagged_sents)
res = counter.count()
try:
print("Palabras misóginas: {}".format(res['misóginia']))
except KeyError:
pass
try:
print("Palabras groseras: {}".format(res['grosería']))
except KeyError:
pass
