NLP之情感分析：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

NLP之情感分析：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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全部代码

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NLP之情感分析：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之情感分析：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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