def get_tweets(data_file):
tweets = []
matrix = pd.read_csv(data_file, sep='\t').as_matrix()
for tweet in matrix:
tweets.append(Tweet(tweet[0], tweet[1], tweet[2], tweet[3]))
return tweets
trainTweets = get_tweets('data/train-CE.tsv')
testTweets = get_tweets('data/test-CE.tsv')
topics = np.unique(pd.read_csv('data/test-CE.tsv', sep='\t').as_matrix()[:, 1])
vect = TfidfVectorizer()
tfidf = vect.fit_transform(get_negAndpos(trainTweets))
eval_res = []
eval_acc = []
for topic in topics:
weights_all = vect.transform([' '.join(Tweet.get_all_messages(testTweets, topic))])
pred_all = tfidf.A.dot(weights_all.T.A)
weights = vect.transform(Tweet.get_all_messages(testTweets, topic))
res = [Tweet.sentiments[x] for x in np.argmax(tfidf.A.dot(weights.T.A) * pred_all, axis=0)]
eval_res += [Evaluate.evaluateC(res, Tweet.get_all_sentiment(testTweets, topic))]
eval_acc += [
np.sum([1 if x == y else 0 for x, y in zip(res, Tweet.get_all_sentiment(testTweets, topic))]) / len(res)]
print("Evaluation " + str(np.average(eval_res)))
print("Evaluation Acc" + str(np.average(eval_acc)))
