import jieba import numpy as np #打开词典文件，返回列表 def open_dict(Dict = 'mini', path=r'/Users/apple888/PycharmProjects/Textming/Sent_Dict/Hownet/'):     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'C:/Users/Administrator/Desktop/Textming/') posdict = open_dict(Dict = 'positive', path= r'C:/Users/Administrator/Desktop/Textming/') negdict = open_dict(Dict = 'negative', path= r'C:/Users/Administrator/Desktop/Textming/') degree_word = open_dict(Dict = '程度级别词语', path= r'C:/Users/Administrator/Desktop/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('。')     for item in seg_sentence:         item.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         sinsitive_count1=0         sinsitive_count2 = 0         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                 sinsitive_count1+=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                 sinsitive_count2+=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                         sinsitive_count1+=1                         sinsitive_count2+=1                         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)         print(score_array)         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])     return score data = '你就是坑人的，什么玩意!你们的手机真不好用！非常生气，我非常郁闷！！！！' data2= '我好开心啊，非常非常非常高兴！今天我得了一百分，我很兴奋开心，愉快，开心' print(sentiment_score(sentiment_score_list(data))) print(sentiment_score(sentiment_score_list(data2)))