BosonNLP情感词典 评论情感分析

from snownlp import SnowNLP
import pandas as pd
from collections import defaultdict
import os
import re
import jieba
import codecs
'''
#读取评论内容的.txt文件
txt = open('C:/Users/24224/Desktop/1.txt',encoding='utf-8')
text = txt.readlines()
print(text)

#确认读取文件成功，并关闭文件节省资源
print('读入成功')
txt.close()

#遍历每一条评论，得到每条评论是positive文本的概率，每条评论计算完成后输出ok确认执行成功
comments = []
comments_score = []
for i in text:
    a1 = SnowNLP(i)
    a2 = a1.sentiments
    comments.append(i)
    comments_score.append(a2)
    print('ok')

#将结果数据框存为.xlsx表格，查看结果及分布
table = pd.DataFrame(comments, comments_score)
print(table)
table.to_excel('C:/Users/24224/Desktop/emotion_analyse.xlsx', sheet_name='result')
#打分范围是[0-1]，此次定义[0,0.5]为负向评论，(0.5,1]为正向评论，观察其分布。


#基于波森情感词典计算情感值
def getscore(text):
    df = pd.read_table(r"BosonNLP_sentiment_score\BosonNLP_sentiment_score.txt", sep=" ", names=['key', 'score'])
    key = df['key'].values.tolist()
    score = df['score'].values.tolist()
    # jieba分词
    segs = jieba.lcut(text,cut_all = False) #返回list
    # 计算得分
    score_list = [score[key.index(x)] for x in segs if(x in key)]
    return sum(score_list)

#读取文件
def read_txt(filename):
    with open(filename,'r',encoding='utf-8')as f:
        txt = f.read()
    return txt
#写入文件
def write_data(filename,data):
    with open(filename,'a',encoding='utf-8')as f:
        f.write(data)

if __name__=='__main__':
    text = read_txt('C:/Users/24224/Desktop/1.txt')
    lists  = text.split('\n')
    i = 0
    for list in lists:
        if list  != '':
            sentiments = round(getscore(list),2)
            #情感值为正数，表示积极；为负数表示消极
            print(list)
            print("情感值：",sentiments)
            if sentiments > 0:
                print("机器标注情感倾向：积极\n")
                s = "机器判断情感倾向：积极\n"
            else:
                print('机器标注情感倾向：消极\n')
                s = "机器判断情感倾向：消极"+'\n'
            sentiment = '情感值：'+str(sentiments)+'\n'
            #文件写入
            filename = 'BosonNLP情感分析结果.txt'
            write_data(filename,'情感分析文本：')
            write_data(filename,list+'\n') #写入待处理文本
            write_data(filename,sentiment) #写入情感值
            #write_data(filename,al_sentiment) #写入机器判断情感倾向
            write_data(filename,s+'\n') #写入人工标注情感
            i = i+1
'''
# 生成stopword表，需要去除一些否定词和程度词汇
stopwords = set()
fr = open('停用词.txt', 'r', encoding='utf-8')

for word in fr:
    stopwords.add(word.strip())  # Python strip() 方法用于移除字符串头尾指定的字符（默认为空格或换行符）或字符序列。
# 读取否定词文件
not_word_file = open('否定词.txt', 'r+', encoding='utf-8')
not_word_list = not_word_file.readlines()
not_word_list = [w.strip() for w in not_word_list]
# 读取程度副词文件
degree_file = open('程度副词.txt', 'r+',encoding='utf-8')
degree_list = degree_file.readlines()
degree_list = [item.split(',')[0] for item in degree_list]

# 生成新的停用词表
with open('stopwords.txt', 'w', encoding='utf-8') as f:
    for word in stopwords:
        if (word not in not_word_list) and (word not in degree_list):
            f.write(word + '\n')
# jieba分词后去除停用词

def seg_word(sentence):
    seg_list = jieba.cut(sentence)
    seg_result = []
    for i in seg_list:
        seg_result.append(i)
    stopwords = set()
    with open('stopwords.txt', 'r',encoding='utf-8') as fr:
        for i in fr:
            stopwords.add(i.strip())
    return list(filter(lambda x: x not in stopwords, seg_result))


# 找出文本中的情感词、否定词和程度副词

def classify_words(word_list):
    # 读取情感词典文件
    sen_file = open('BosonNLP_sentiment_score\BosonNLP_sentiment_score.txt', 'r+', encoding='utf-8')
    # 获取词典文件内容
    sen_list = sen_file.readlines()
    # 创建情感字典
    sen_dict = defaultdict()
    # 读取词典每一行的内容，将其转换成字典对象，key为情感词，value为其对应的权重
    for i in sen_list:
        if len(i.split(' ')) == 2:
            sen_dict[i.split(' ')[0]] = i.split(' ')[1]
    # 读取否定词文件

    not_word_file = open('否定词.txt', 'r+', encoding='utf-8')
    not_word_list = not_word_file.readlines()
    # 读取程度副词文件
    degree_file = open('程度副词.txt', 'r+', encoding='utf-8')
    degree_list = degree_file.readlines()
    degree_dict = defaultdict()
    for i in degree_list:
        degree_dict[i.split(',')[0]] = i.split(',')[0]
    sen_word = dict()
    not_word = dict()
    degree_word = dict()
    # 分类
    for i in range(len(word_list)):
        word = word_list[i]
        if word in sen_dict.keys() and word not in not_word_list and word not in degree_dict.keys():
            # 找出分词结果中在情感字典中的词
            sen_word[i] = sen_dict[word]
        elif word in not_word_list and word not in degree_dict.keys():
            # 分词结果中在否定词列表中的词
            not_word[i] = -1
        elif word in degree_dict.keys():
            # 分词结果中在程度副词中的词
            degree_word[i] = degree_dict[word]
    # 关闭打开的文件
    sen_file.close()
    not_word_file.close()
    degree_file.close()
    # 返回分类结果
    return sen_word, not_word, degree_word

# 计算情感词的分数
def score_sentiment(sen_word, not_word, degree_word, seg_result):
    # 权重初始化为1
    W = 1
    score = 0
    # 情感词下标初始化
    sentiment_index = -1
    # 情感词的位置下标集合
    sentiment_index_list = list(sen_word.keys())
    # 遍历分词结果
    for i in range(0, len(seg_result)):
        # 如果是情感词
        if i in sen_word.keys():
            # 权重*情感词得分
            score += W * float(sen_word[i])
            # 情感词下标加一，获取下一个情感词的位置
            sentiment_index += 1
            if sentiment_index < len(sentiment_index_list) - 1:
                # 判断当前的情感词与下一个情感词之间是否有程度副词或否定词
                for j in range(sentiment_index_list[sentiment_index], sentiment_index_list[sentiment_index + 1]):
                    # 更新权重，如果有否定词，权重取反
                    if j in not_word.keys():
                        W *= -1
                    elif j in degree_word.keys():
                        W *= float(degree_word[j])
        # 定位到下一个情感词
        if sentiment_index < len(sentiment_index_list) - 1:
            i = sentiment_index_list[sentiment_index + 1]
    return score


# 计算得分
def sentiment_score(sentence):
    # 1.对文档分词
    seg_list = seg_word(sentence)
    # 2.将分词结果转换成字典，找出情感词、否定词和程度副词
    sen_word, not_word, degree_word = classify_words(seg_list)
    # 3.计算得分
    score = score_sentiment(sen_word, not_word, degree_word, seg_list)
    return score
#读取文件
def read_txt(filename):
    with open(filename,'r',encoding='utf-8')as f:
        txt = f.read()
    return txt
def write_data(filename,data):
    with open(filename,'a',encoding='utf-8')as f:
        f.write(data)
#基于波森情感词典计算情感值
text = read_txt('C:/Users/24224/Desktop/1.txt')
lists  = text.split('\n')
i = 0
for l in lists:
    if l  != '':
        sentiments =sentiment_score(l)
            #情感值为正数，表示积极；为负数表示消极
        print("情感值：",sentiments)
        if sentiments > 0:
            print(l)
            print("机器标注情感倾向：积极\n")
            s = "机器判断情感倾向：积极\n"
        else:
            print(l)
            print('机器标注情感倾向：消极\n')
            s = "机器判断情感倾向：消极"+'\n'
        sentiment = '情感值：'+str(sentiments)+'\n'
            #文件写入
        filename = 'BosonNLP情感分析结果.txt'
        write_data(filename,'情感分析文本：')
        write_data(filename,l+'\n') #写入待处理文本
        write_data(filename,sentiment) #写入情感值
            #write_data(filename,al_sentiment) #写入机器判断情感倾向
        write_data(filename,s+'\n') #写入人工标注情感
        i = i+1








1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242