def preprocess(key, tweet):
id_tweet_map, tweet_id_map = get_tweet_map()
id_tokenised_tweet_map = get_id_tokenised_tweet_map()
tokenized_tweet = id_tokenised_tweet_map[key]
# Get emoticons, hashtags, mentions and URLs for a given tweet.
emoticons = getemoticons(tweet)
hashtags = gethashtags(tokenized_tweet)
mentions = getmentions(tokenized_tweet)
urls = geturls(tweet)
# Get character n-grams (n=1-3) for a given tweet.
char_n_grams = getcharngrams(tweet)
stop_words = get_stop_words()
# Replace emoticons, hashtags, mentions and URLs in a tweet.
processed_tokenized_tweet = processtweet(tokenized_tweet, stop_words)
processed_tweet = " ".join(processed_tokenized_tweet[0:])
# # Get count of each punctuation and then remove them from the tweet.
# pucntuations_count = getpunctuations(processed_tweet)
# processed_tweet = removepunctuations(processed_tweet)
# processed_tokenized_tweet = tokenize_tweet(processed_tweet)
# Remove stop words from the tweet.
# processed_tokenized_tweet = removestopwords(processed_tokenized_tweet)
# processed_tweet = " ".join(processed_tokenized_tweet[0:])
# Get word n-grams (n=1-3) for the tweet.
word_n_grams = getwordngrams(processed_tokenized_tweet)
return emoticons, hashtags, mentions, urls, char_n_grams, word_n_grams
def findfeatureproperties():
id_tweet_map, tweet_id_map = get_tweet_map()
id_tokenised_tweet_map = get_id_tokenised_tweet_map()
id_truth_map = get_id_truth_map()
# Get all char n-grams (n=1-5) from training set and create an index for each of them.
char_n_grams = getallcharngrams(id_tweet_map)
getcharngramsindex(char_n_grams)
processed_id_tweet_map, processed_id_tokenised_tweet_map = processtweetforwordngrams(
id_tweet_map, id_tokenised_tweet_map)
# Get all word n-grams (n=1-3) from training set and create an index for each of them.
word_n_grams = getallwordngrams(processed_id_tweet_map,
processed_id_tokenised_tweet_map)
getwordngramsindex(word_n_grams)
token_count, hashtag_count = gettargetwords(
id_tweet_map, processed_id_tweet_map, id_tokenised_tweet_map,
processed_id_tokenised_tweet_map, id_truth_map)
truth_top_hashtags = gettophashtags(hashtag_count)
truth_top_hi_tokens, truth_top_en_tokens, truth_top_rest_tokens = gettoptokens(
token_count)
fp = open('data.txt', 'w')
pickle.dump(6, fp)
pickle.dump(char_n_grams_index, fp)
pickle.dump(word_n_grams_index, fp)
pickle.dump(truth_top_hashtags, fp)
pickle.dump(truth_top_hi_tokens, fp)
pickle.dump(truth_top_en_tokens, fp)
pickle.dump(truth_top_rest_tokens, fp)
fp.close()
def preprocess(key, tweet):
id_tweet_map, tweet_id_map = get_tweet_map()
id_tokenised_tweet_map = get_id_tokenised_tweet_map()
tokenized_tweet = id_tokenised_tweet_map[key]
# Get emoticons, hashtags, mentions and URLs for a given tweet.
emoticons = getemoticons(tweet)
hashtags = gethashtags(tokenized_tweet)
mentions = getmentions(tokenized_tweet)
urls = geturls(tweet)
capitalcount = getCapitalcount(tweet, tokenized_tweet)
# Get character n-grams (n=1-3) for a given tweet.
idiom = getidiom(tweet)
char_n_grams = getcharngrams(tweet)
puncts = getpuncts(tweet)
#length=len(tweet)
stop_words = get_stop_words()
# Replace emoticons, hashtags, mentions and URLs in a tweet.
processed_tokenized_tweet = processtweet(tokenized_tweet, stop_words)
processed_tweet = " ".join(processed_tokenized_tweet[0:])
# Get word n-grams (n=1-5) for the tweet.
word_n_grams = getwordngrams(processed_tokenized_tweet)
#print("Done execution")
return emoticons, hashtags, mentions, urls, char_n_grams, word_n_grams, idiom, capitalcount, puncts
# tweet = "En el día @shyamli de hoy #27óS sólo me @sahil sale del alma gritar ¡¡VIVA ESPAÑA! ! http://t.co/w9Bmsf4TUK :) (: #NLP"
def train_and_test():
findfeatureproperties()
id_truth_map = get_id_truth_map()
train_truth_feature_map = getfeaturevectorforalltweets()
truth_index = {'YES': 0, 'NO': 1, 0: 'YES', 1: 'NO'}
id_tweet_map = get_tweet_map()
tenfoldcrossvalidation(train_truth_feature_map, id_truth_map, truth_index, id_tweet_map)
def getfeaturevectorforalltweets():
id_tweet_map, tweet_id_map = get_tweet_map()
# print len(id_tweet_map)
id_tweet_map = dict(sorted(id_tweet_map.items(), key=operator.itemgetter(0)))
train_truth_feature_map = {}
count = 1
for key, tweet in id_tweet_map.iteritems():
truth_feature_vector = getfeaturevector(key, tweet)
train_truth_feature_map[key] = truth_feature_vector
# print count
count += 1
return train_truth_feature_map
def train_and_test():
findfeatureproperties()
id_stance_map = get_id_stance_map()
train_stance_feature_map = getfeaturevectorforalltweets()
stance_index = {
'FAVOR': 0,
'AGAINST': 1,
'NONE': 2,
0: 'FAVOR',
1: 'AGAINST',
2: 'NONE'
}
id_tweet_map = get_tweet_map()
tenfoldcrossvalidation(train_stance_feature_map, id_stance_map,
stance_index, id_tweet_map)
def preprocess(key, tweet):
id_tweet_map, tweet_id_map = get_tweet_map()
id_tokenised_tweet_map = get_id_tokenised_tweet_map()
tokenized_tweet = id_tokenised_tweet_map[key]
# Get emoticons, hashtags, mentions and URLs for a given tweet.
emoticons = getemoticons(tweet)
hashtags = gethashtags(tokenized_tweet)
mentions = getmentions(tokenized_tweet)
urls = geturls(tweet)
# Get character n-grams (n=1-3) for a given tweet.
char_n_grams = getcharngrams(tweet)
stop_words = get_stop_words()
# Replace emoticons, hashtags, mentions and URLs in a tweet.
processed_tokenized_tweet = processtweet(tokenized_tweet, stop_words)
processed_tweet = " ".join(processed_tokenized_tweet[0:])
# Get word n-grams (n=1-5) for the tweet.
word_n_grams = getwordngrams(processed_tokenized_tweet)
return emoticons, hashtags, mentions, urls, char_n_grams, word_n_grams
