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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
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