def classify_replies_tweets(self):
tdb = TweetDB()
tweets = list(tdb.all_replies())
tweets_with_emotion_indexes, total = self.classify_sentences([t.text for t in tweets])
tweets_with_emotion = [int(tweets[i].parent_tweet.retweet_count) for i in tweets_with_emotion_indexes]
tweets_with_emotion_mean = np.array(tweets_with_emotion).mean() if tweets_with_emotion else 0
self.logger.info('%d of %d on %s category (retweets average: %.2f)' % (
len(tweets_with_emotion), total, self.emotion_label, tweets_with_emotion_mean))
def predict_replies(filepath, **kwargs):
identified_replies = []
files_tec = [os.path.join('output', '%s-tec.pickle' % e) for e in emotions]
for i, f in enumerate(files_tec):
# nb = SingleEmotionSemEvalNaiveBayes(emotion_index=i, filename=f)
nb = SingleEmotionTECNaiveBayes(emotion_name=emotions[i], filename=f)
tdb = TweetDB()
tweets = list(tdb.all_replies())
tweets_with_emotion_indexes, total = nb.classify_sentences(
[t.text for t in tweets])
identified_replies.append(tweets_with_emotion_indexes)
print('%d of %d on %s category' %
(len(tweets_with_emotion_indexes), total, emotions[i]))
# Count how many additional emotions each tweet has, and prints to the console the average and median for each emotion
for index, tweets_indexes in enumerate(identified_replies):
other_tweets_indexes = [
ti for tis in identified_replies for ti in tis
if tis != tweets_indexes
]
repeated_tweets_indexes = [
other_tweets_indexes.count(ti) for ti in tweets_indexes
if ti in other_tweets_indexes
]
one_emotion_tweets = [
tweets[ti] for ti in tweets_indexes
if ti not in other_tweets_indexes
]
print(
'%s: %d out of %d have been identified with another emotion as well (avg: %.2f, mdn: %.2f)'
% (emotions[index], len(repeated_tweets_indexes),
len(tweets_indexes), np.mean(repeated_tweets_indexes),
np.median(repeated_tweets_indexes)))
print('%d tweets have been identified only with %s' %
((len(tweets_indexes) - len(repeated_tweets_indexes)),
emotions[index]))
get_parent_tweets_metrics(one_emotion_tweets, emotions[index])
count_repeated_emotions(tweets, identified_replies)