sentiment analysis(very ish est less)

import jiebaimport 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4verydict = degree_word[degree_word.index(‘very‘)+1 : degree_word.index(‘more‘)]#权重3moredict = degree_word[degree_word.index(‘more‘)+1 : degree_word.index(‘ish‘)]#权重2ishdict = 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)))

原文地址：https://www.cnblogs.com/rabbittail/p/8336291.html