def triple_classifier(tweet):
'''
输出结果：
0 中性
1 积极
2 生气
3 焦虑
4 悲伤
5 厌恶
6 消极其他
'''
sentiment = 0
if isinstance(tweet['text'],unicode):
text = tweet['text']
else:
text = tweet['text'].decode('utf-8')
keywords_list = []
emoticon_sentiment = emoticon(text.encode('utf-8'))
if emoticon_sentiment != MIDDLE:
entries = cut(cut_str, text.encode('utf-8'))
entry = [e.decode('utf-8', 'ignore') for e in entries]
keywords_list = entry
if emoticon_sentiment == POSITIVE:
sentiment = emoticon_sentiment
text = u''
else:
sentiment = flow_psychology_classfiy(text.encode('utf-8'))
if sentiment == 0:
sentiment = 6
text = u''
if text != u'':
entries = cut(cut_str, text.encode('utf-8'))
entry = [e.decode('utf-8', 'ignore') for e in entries]
keywords_list = entry
bow = dictionary_1.doc2bow(entry)
s = [1, 1]
for pair in bow:
s[0] = s[0] * (step1_score[pair[0]][0] ** pair[1])
s[1] = s[1] * (step1_score[pair[0]][1] ** pair[1])
if s[0] <= s[1]:
bow = dictionary_2.doc2bow(entry)
s2 = [1, 1]
for pair in bow:
s2[0] = s2[0] * (step2_score[pair[0]][0] ** pair[1])
s2[1] = s2[1] * (step2_score[pair[0]][1] ** pair[1])
if s2[0] > s2[1]:
sentiment = POSITIVE
else:
sentiment = flow_psychology_classfiy(text.encode('utf-8'))
if sentiment == 0:
sentiment = 6
else:
sentiment = MIDDLE
return sentiment
def triple_classifier(tweet):
'''
输出结果：
0 中性
1 积极
2 生气
3 焦虑
4 悲伤
5 厌恶
6 消极其他
'''
sentiment = 0
if isinstance(tweet['text'],unicode):
text = tweet['text']
else:
text = tweet['text'].decode('utf-8')
keywords_list = []
emoticon_sentiment = emoticon(text.encode('utf-8'))
if emoticon_sentiment != MIDDLE:
entries = cut(cut_str, text.encode('utf-8'))
entry = [e.decode('utf-8', 'ignore') for e in entries]
keywords_list = entry
if emoticon_sentiment == POSITIVE:
sentiment = emoticon_sentiment
text = u''
else:
sentiment = flow_psychology_classfiy(text.encode('utf-8'))
if sentiment == 0:
sentiment = 6
text = u''
if text != u'':
entries = cut(cut_str, text.encode('utf-8'))
entry = [e.decode('utf-8', 'ignore') for e in entries]
keywords_list = entry
bow = dictionary_1.doc2bow(entry)
s = [1, 1]
for pair in bow:
s[0] = s[0] * (step1_score[pair[0]][0] ** pair[1])
s[1] = s[1] * (step1_score[pair[0]][1] ** pair[1])
if s[0] <= s[1]:
bow = dictionary_2.doc2bow(entry)
s2 = [1, 1]
for pair in bow:
s2[0] = s2[0] * (step2_score[pair[0]][0] ** pair[1])
s2[1] = s2[1] * (step2_score[pair[0]][1] ** pair[1])
if s2[0] > s2[1]:
sentiment = POSITIVE
else:
sentiment = flow_psychology_classfiy(text.encode('utf-8'))
if sentiment == 0:
sentiment = 6
else:
sentiment = MIDDLE
return sentiment
def triple_classifier(tweet):
"""
输出结果：
0 中性
1 积极
2 生气
3 焦虑
4 悲伤
5 厌恶
6 消极其他
"""
sentiment = 0
text = tweet["text"] # encode
keywords_list = []
emoticon_sentiment = emoticon(text)
if emoticon_sentiment != 0:
entries = cut(cut_str, text)
entry = [e.decode("utf-8", "ignore") for e in entries]
keywords_list = entry
if emoticon_sentiment == HAPPY:
sentiment = emoticon_sentiment
text = ""
else:
sentiment = flow_psychology_classfiy(text)
if sentiment == 0:
sentiment = emoticon_sentiment
text = ""
if text != "":
entries = cut(cut_str, text)
entry = [e.decode("utf-8", "ignore") for e in entries]
keywords_list = entry
bow = dictionary_1.doc2bow(entry)
s = [1, 1]
for pair in bow:
s[0] = s[0] * (step1_score[pair[0]][0] ** pair[1])
s[1] = s[1] * (step1_score[pair[0]][1] ** pair[1])
if s[0] <= s[1]:
bow = dictionary_2.doc2bow(entry)
s = [1, 1, 1]
for pair in bow:
s[0] = s[0] * (step2_score[pair[0]][0] ** pair[1])
s[1] = s[1] * (step2_score[pair[0]][1] ** pair[1])
s[2] = s[2] * (step2_score[pair[0]][2] ** pair[1])
if s[0] > s[1] and s[0] > s[2]:
sentiment = HAPPY
else:
sentiment = flow_psychology_classfiy(text)
if sentiment == 0:
if s[1] > s[0] and s[1] > s[2]:
sentiment = SAD
elif s[2] > s[1] and s[2] > s[0]:
sentiment = ANGRY
else:
sentiment = 6
else:
sentiment = 0
return sentiment
def triple_classifier(tweet):
'''
输出结果：
0 中性
1 积极
2 生气
3 焦虑
4 悲伤
5 厌恶
6 消极其他
'''
sentiment = 0
# text = tweet['text'] # encode
text = tweet['text_ch']
keywords_list = []
try:
emoticon_sentiment = emoticon(text)
if emoticon_sentiment != 0:
entries = cut(cut_str, text.encode('utf-8'))
entry = [e.decode('utf-8', 'ignore') for e in entries]
keywords_list = entry
if emoticon_sentiment == HAPPY:
sentiment = emoticon_sentiment
text = u''
else:
sentiment = flow_psychology_classfiy(text)
if sentiment == 0:
sentiment = emoticon_sentiment
text = u''
if text != u'':
entries = cut(cut_str, text.encode('utf-8'))
entry = [e.decode('utf-8', 'ignore') for e in entries]
keywords_list = entry
bow = dictionary_1.doc2bow(entry)
s = [1, 1]
for pair in bow:
s[0] = s[0] * (step1_score[pair[0]][0]**pair[1])
s[1] = s[1] * (step1_score[pair[0]][1]**pair[1])
if s[0] <= s[1]:
bow = dictionary_2.doc2bow(entry)
s = [1, 1, 1]
for pair in bow:
s[0] = s[0] * (step2_score[pair[0]][0]**pair[1])
s[1] = s[1] * (step2_score[pair[0]][1]**pair[1])
s[2] = s[2] * (step2_score[pair[0]][2]**pair[1])
if s[0] > s[1] and s[0] > s[2]:
sentiment = HAPPY
else:
sentiment = flow_psychology_classfiy(text)
if sentiment == 0:
if s[1] > s[0] and s[1] > s[2]:
sentiment = SAD
elif s[2] > s[1] and s[2] > s[0]:
sentiment = ANGRY
else:
sentiment = 6
else:
sentiment = 0
except:
pass
return sentiment, keywords_list
