NLP之TEA:基于python编程(jieba库)实现中文文本情感分析(得到的是情感评分)之全部代码
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NLP之TEA:基于python编程(jieba库)实现中文文本情感分析(得到的是情感评分)
NLP之TEA:基于python编程(jieba库)实现中文文本情感分析(得到的是情感评分)之全部代码
全部代码
# coding:utf-8
import jieba
import numpy as np
#打开词典文件,返回列表
def open_dict(Dict = 'hahah', path=r'data/Textming'):
path = path + '%s.txt' % Dict
dictionary = open(path, 'r', encoding='utf-8')
dict = []
for word in dictionary:
word = word.strip('\n')
dict.append(word)
return dict
def judgeodd(num):
if (num % 2) == 0:
return 'even'
else:
return 'odd'
#注意,这里你要修改path路径。
deny_word = open_dict(Dict = '否定词', path= r'F:/File_Python/Resources/data/Textming/')
posdict = open_dict(Dict = 'positive', path= r'F:/File_Python/Resources/data/Textming/')
negdict = open_dict(Dict = 'negative', path= r'F:/File_Python/Resources/data/Textming/')
degree_word = open_dict(Dict = '程度级别词语', path= r'F:/File_Python/Resources/data/Textming/')
mostdict = degree_word[degree_word.index('extreme')+1 : degree_word.index('very')]#权重4,即在情感词前乘以4
verydict = degree_word[degree_word.index('very')+1 : degree_word.index('more')]#权重3
moredict = degree_word[degree_word.index('more')+1 : degree_word.index('ish')]#权重2
ishdict = degree_word[degree_word.index('ish')+1 : degree_word.index('last')]#权重0.5
def sentiment_score_list(dataset):
seg_sentence = dataset.split('。')
count1 = []
count2 = []
for sen in seg_sentence: #循环遍历每一个评论
segtmp = jieba.lcut(sen, cut_all=False) #把句子进行分词,以列表的形式返回
i = 0 #记录扫描到的词的位置
a = 0 #记录情感词的位置
poscount = 0 #积极词的第一次分值
poscount2 = 0 #积极词反转后的分值
poscount3 = 0 #积极词的最后分值(包括叹号的分值)
negcount = 0
negcount2 = 0
negcount3 = 0
for word in segtmp:
if word in posdict: # 判断词语是否是情感词
poscount += 1
c = 0
for w in segtmp[a:i]: # 扫描情感词前的程度词
if w in mostdict:
poscount *= 4.0
elif w in verydict:
poscount *= 3.0
elif w in moredict:
poscount *= 2.0
elif w in ishdict:
poscount *= 0.5
elif w in deny_word:
c += 1
if judgeodd(c) == 'odd': # 扫描情感词前的否定词数
poscount *= -1.0
poscount2 += poscount
poscount = 0
poscount3 = poscount + poscount2 + poscount3
poscount2 = 0
else:
poscount3 = poscount + poscount2 + poscount3
poscount = 0
a = i + 1 # 情感词的位置变化
elif word in negdict: # 消极情感的分析,与上面一致
negcount += 1
d = 0
for w in segtmp[a:i]:
if w in mostdict:
negcount *= 4.0
elif w in verydict:
negcount *= 3.0
elif w in moredict:
negcount *= 2.0
elif w in ishdict:
negcount *= 0.5
elif w in degree_word:
d += 1
if judgeodd(d) == 'odd':
negcount *= -1.0
negcount2 += negcount
negcount = 0
negcount3 = negcount + negcount2 + negcount3
negcount2 = 0
else:
negcount3 = negcount + negcount2 + negcount3
negcount = 0
a = i + 1
elif word == '!' or word == '!': ##判断句子是否有感叹号
for w2 in segtmp[::-1]: # 扫描感叹号前的情感词,发现后权值+2,然后退出循环
if w2 in posdict or negdict:
poscount3 += 2
negcount3 += 2
break
i += 1 # 扫描词位置前移
# 以下是防止出现负数的情况
pos_count = 0
neg_count = 0
if poscount3 < 0 and negcount3 > 0:
neg_count += negcount3 - poscount3
pos_count = 0
elif negcount3 < 0 and poscount3 > 0:
pos_count = poscount3 - negcount3
neg_count = 0
elif poscount3 < 0 and negcount3 < 0:
neg_count = -poscount3
pos_count = -negcount3
else:
pos_count = poscount3
neg_count = negcount3
count1.append([pos_count, neg_count])
count2.append(count1)
count1 = []
return count2
def sentiment_score(senti_score_list):
score = []
for review in senti_score_list:
score_array = np.array(review)
Pos = np.sum(score_array[:, 0])
Neg = np.sum(score_array[:, 1])
AvgPos = np.mean(score_array[:, 0])
AvgPos = float('%.1f'%AvgPos)
AvgNeg = np.mean(score_array[:, 1])
AvgNeg = float('%.1f'%AvgNeg)
StdPos = np.std(score_array[:, 0])
StdPos = float('%.1f'%StdPos)
StdNeg = np.std(score_array[:, 1])
StdNeg = float('%.1f'%StdNeg)
score.append([Pos, Neg, AvgPos, AvgNeg, StdPos, StdNeg]) #积极、消极情感值总和(最重要),积极、消极情感均值,积极、消极情感方差。
return score
def EmotionByScore(data):
result_list=sentiment_score(sentiment_score_list(data))
return result_list[0][0],result_list[0][1]
def JudgingEmotionByScore(Pos, Neg):
if Pos > Neg:
str='1'
elif Pos < Neg:
str='-1'
elif Pos == Neg:
str='0'
return str
data1= '今天上海的天气真好!我的心情非常高兴!如果去旅游的话我会非常兴奋!和你一起去旅游我会更加幸福!'
data2= '救命,你是个坏人,救命,你不要碰我,救命,你个大坏蛋!'
data3= '美国华裔科学家,祖籍江苏扬州市高邮县,生于上海,斯坦福大学物理系,电子工程系和应用物理系终身教授!'
print(sentiment_score(sentiment_score_list(data1)))
print(sentiment_score(sentiment_score_list(data2)))
print(sentiment_score(sentiment_score_list(data3)))
a,b=EmotionByScore(data1)
emotion=JudgingEmotionByScore(a,b)
print(emotion)
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NLP之TEA:基于python编程(jieba库)实现中文文本情感分析(得到的是情感评分)之全部代码
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