# print("found smilies total:")
# print sum(Counter(foundEmosAndSmilies).values())
# print("found smilies examples ordered:")
# print Counter(foundEmosAndSmilies)
# print("found smilies top50")
# print Counter.most_common(Counter(foundEmosAndSmilies), 50)
# print("total number of sentences with smilies:")
# print numOfTotalSentences
"""
Classify
"""
for message in messages:
messagecounter += 1
# for every sentence
score, words = emoCount.score(message)
foundEmosAndSmilies = foundEmosAndSmilies + words
if len(words) != 0:
numOfTotalSentences += 1
print(words)
# in the end
print("Messages total:")
print messagecounter
print("found smilies total:")
print sum(Counter(foundEmosAndSmilies).values())
print("found smilies examples ordered:")
print Counter(foundEmosAndSmilies)
print("found smilies top50")
# if labels[index] == score:
# rightSentimentCount += 1
#
# predictedlabels.append(score)
"""
Smilie scoring
"""
from count_smilies_class import Emo
emoCount = Emo(language="twitchstandard", emoticons=False, secondemo="emoticons")
for index, sent in enumerate(messages):
sentenceCount += 1
# print sentenceCount
print sent
score, words = emoCount.score(sent) # bisschen umgeschrieben, dass auch die missing words ausgegeben werden
a = emoCount.find_all(sent)
if len(a) == 0:
emptysents += 1
# print score
if score > 0:
score = float(1.0)
elif score < 0:
score = float(-1.0)
print labels[index], score
if labels[index] == score:
rightSentimentCount += 1
predictedlabels.append(score)
print rightSentimentCount
