基于情感词典的文本情感分析
原代码来源：https://blog.csdn.net/lom9357bye/article/details/79058946
本文是对原代码的几个bug进行了修复，用到的词典可由以上链接下载
import codecs
from collections import defaultdict
import jieba
import xlrd
# 分词，去除停用词
def seg_word(sentence):
# 分词
seg_list = jieba.cut(sentence)
seg_result = []
for w in seg_list:
seg_result.append(w)
# 读取停用词
stopwords = set() # 集合
fr = codecs.open('stopwords.txt', 'r', 'utf-8')
for word in fr:
stopwords.add(word.strip())
fr.close()
# 去除停用词
return list(filter(lambda x: x not in stopwords, seg_result))
# 对分词结果分类：情感词、否定词、程度副词
# key为索引，value为权值
def classify_words(word_list):
# 读取情感字典
sen_file = open('BosonNLP_sentiment_score.txt', 'r+', encoding='utf-8')
# 获取字典内容
# 去除'\n'
sen_list = sen_file.read().splitlines()
# 创建情感字典
sen_dict = defaultdict()
# 读取字典文件每一行内容，将其转换为字典对象，key为情感词，value为对应的分值
for s in sen_list:
# 对每一行内容根据空格分隔，索引0是情感词，1是情感分值
if len(s.split(' ')) == 2:
sen_dict[s.split(' ')[0]] = s.split(' ')[1]
# 读取否定词文件
not_word_file = open('notDic.txt', 'r+', encoding='utf-8')
# 否定词没有分值,使用列表
not_word_list = not_word_file.read().splitlines()
# 读取程度副词文件
degree_file = open('degree.txt', 'r+', encoding='utf-8')
degree_list = degree_file.read().splitlines()
degree_dic = defaultdict()
# 程度副词转为字典对象，key为词，value为权值
for d in degree_list:
degree_dic[d.split(',')[0]] = d.split(',')[1]
# 分类结果，词语索引为key，分值为value，否定词分值为-1
sen_word = dict()
not_word = dict()
degree_word = dict()
# 分类
for word in word_list:
if word in sen_dict.keys() and word not in not_word_list and word not in degree_dic.keys():
# 找出分词结果中在情感字典中的词
sen_word[word] = sen_dict[word]
elif word in not_word_list and word not in degree_dic.keys():
# 分词结果中在否定词列表中的词
not_word[word] = -1
elif word in degree_dic.keys():
# 分词结果中在程度副词中的词
degree_word[word] = degree_dic[word]
sen_file.close()
degree_file.close()
not_word_file.close()
# 将分类结果返回
# 词语索引为key，分值为value，否定词分值为 - 1
return sen_word, not_word, degree_word
# 计算每个情感词得分，再相加
def score_sentiment(sen_word, not_word, degreen_word, seg_result):
# 权重初始化为1
W = 1
score = 0
# 遍历分词结果
for i in range(0, len(seg_result)):
# 若是程度副词
if seg_result[i] in degreen_word.keys():
W *= float(degreen_word[seg_result[i]])
# 若是否定词
elif seg_result[i] in not_word.keys():
W *= -1
elif seg_result[i] in sen_word.keys():
score += float(W) * float(sen_word[seg_result[i]])
W = 1
return score
# 调度各函数
def sentiment_score(sentence):
# 1.分词
seg_list = seg_word(sentence)
# 2.将分词结果转为dic,再分类
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
if __name__ == '__main__':
score=sentiment_score('我很开心。')