基于NRC词典的离散情感分析（含有程度词和否定词）_情感词中的带程度词

NRC词典的加载方法和分词方法之前的文章有写，直接套用即可。

具体函数参考了这篇文章：基于情感词典的文本情感分析(附代码)_Petrichoryi的博客-CSDN博客_bosonnlp情感词典

我相当于是把这篇文章里的正负情感改成了NRC词典中的八种离散情感，进行逐一计算，具体代码如下：

not_word_file = open('E:/data/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，里面包含表示程度副词程度的数字
degree_file = open('E:/data/程度副词.txt','r+',encoding='utf-8')
degree_list = degree_file.readlines()
degree_list = [item.strip().split(',') for item in degree_list]
print(not_word_list)
print(degree_list)

from collections import defaultdict
def classify_words(word_list,Sentiment):
    sen_dict = defaultdict()
    for i in Sentiment:
        sen_dict[i] = 1   #因为用的是NRC词典中的情感词汇，没有具体的情感程度，都定为1
 
    degree_dict = defaultdict()
    for i in degree_list:
        degree_dict[i[0]] = i[1]
 
    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]
    return sen_word,not_word,degree_word

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 = 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

def sentiment_score(sentence,Sentiment):
    #1.对文档分词
    seg_list = seg_depart(sentence)  #分词函数之前文章有写
    #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,Sentiment)
    #3.计算得分
    score = score_sentiment(sen_word,not_word,degree_word,seg_list)
    return score

#将NRC词典中所有离散情感词汇整理成二维列表
Senti_list8 = [Anger,Anticipation,Disgust,Fear,Joy,Sadness,Surprise,Trust]

#将文档中句子的八种情绪结果转换为字典
all_senti = dict()
i = 0
for senti in Senti_list8:
    filename = r'E:\data\test1.csv'
    senti_count = []
    with open(filename, 'r', encoding='UTF-8') as csvfile:
        next(csvfile)
        reader = csv.reader(csvfile)
        for row in reader:
            score = sentiment_score(row[0],Sentiment=senti)
            senti_count.append(score)
        print(senti_count)
    all_senti[i]=senti_count
    i = i+1

#存为csv
test = pd.DataFrame.from_dict(all_senti)
test.to_csv(r"E:\data\test1_result2.csv")