# -*- coding:utf-8 -*
#本代码是在jupyter notebook上实现，author:huzhifei， create time:2018/8/14
#本脚本主要实现了基于python通过已有的情感词典对文本数据做的情感分析的项目目的

#导入对应的包及相关的自定义的jieba词典
import jieba
import numpy as np
jieba.load_userdict("C:\\Users\\Desktop\\中文分词词库整理\\中文分词词库整理\\百度分词词库.txt") 

# 打开词典文件，返回列表
def open_dict(Dict='hahah',path = 'C:\\Users\\Desktop\\Textming\\'):
    path = path + '%s.txt' %Dict
    dictionary = open(path, 'r', encoding='utf-8',errors='ignore')
    dict = []
    for word in dictionary:
        word = word.strip('\n')
        dict.append(word)
    return dict

def judgeodd(num):  #往情感词前查找否定词，找完全部否定词，若数量为奇数，乘以-1，若数量为偶数，乘以1.
    if num % 2 == 0:
        return 'even'
    else:
        return 'odd'


deny_word = open_dict(Dict='deny')#否定词词典
posdict = open_dict(Dict='positive')#积极情感词典
negdict = open_dict(Dict = 'negative')#消极情感词典

degree_word = open_dict(Dict = 'degree',path='C:\\Users\\AAS-1413\\Desktop\\Textming\\')#程度词词典

#为程度词设置权重
mostdict = degree_word[degree_word.index('extreme')+1: degree_word.index('very')] #权重4，即在情感前乘以3
verydict = degree_word[degree_word.index('very')+1: degree_word.index('more')] #权重3
moredict = degree_word[degree_word.index('more')+1: degree_word.index('ish')]#权重2
ishdict = degree_word[degree_word.index('ish')+1: degree_word.index('last')]#权重0.5
seg_sentence=[]


def sentiment_score_list(data):
    for i in data:
        seg_sentence.append(i.replace(' ','，'))#去除逗号后的评论数据集
    #seg_sentence=data.replace(' ','，').split(',')#以逗号分隔
    count1 = []
    count2 = []
    for sen in seg_sentence:
        #print(sen)# 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
        #print(segtmp)
        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
        poscount2 = 0 # 积极反转后的分值
        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: # 判断词语是否是积极情感词
                poscount +=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
            elif word in negdict: # 消极情感的分析，与上面一致
                negcount += 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:
                        poscount3 += 2
                    elif w2 in negdict:
                        negcount3 += 2
                    else:
                        poscount3 +=0
                        negcount3 +=0
                        break
            else:
                poscount3=0
                negcount3=0
            i += 1

            # 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 = -pos_count
                pos_count = -neg_count
            else:
                pos_count = poscount3
                neg_count = negcount3
            count1.append([pos_count,neg_count]) #返回每条评论打分后的列表
            #print(count1)
        count2.append(count1)
        count1=[]
        #print(count2)
    return count2  #返回所有评论打分后的列表

def sentiment_score(senti_score_list):#分析完所有评论后，正式对每句评论打情感分
    #score = []
    s=''
    w=''
    for review in senti_score_list:#senti_score_list
        #print(review)
        score_array =  np.array(review)
        #print(score_array)
        Pos = np.sum(score_array[:,0])#积极总分
        Neg = np.sum(score_array[:,1])#消极总分
        AvgPos = np.mean(score_array[:,0])#积极情感均值
        AvgPos = float('%.lf' % AvgPos)
        AvgNeg = np.mean(score_array[:, 1])#消极情感均值
        AvgNeg = float('%.1f' % AvgNeg)
        StdPos = np.std(score_array[:, 0])#积极情感方差
        StdPos = float('%.1f' % StdPos)
        StdNeg = np.std(score_array[:, 1])#消极情感方差
        StdNeg = float('%.1f' % StdNeg)
        #s+=([Pos,Neg,AvgPos,AvgNeg,StdPos,StdNeg]))
        s+='\n'+str([Pos, Neg])
        #score.append([Pos,Neg])
        res=Pos-Neg
        if res>0:
            w+='\n'+'好评'
            print ('该条评论是：好评')
        elif res<0:
            w+='\n'+'差评'
            print ('该条评论是：差评')
        else:
            w+='\n'+'中评'
            print ('该条评论是：中评')
    #print(w)
	return w



#读取要做情感分析的文本
data=open("content.txt","r",errors='ignore')


#调用函数做实体分析
sentiment_score(sentiment_score_list(data))

#将函数返回结果存入txt中
f=open('s.txt','w',errors='ignore')
f.write(sentiment_score(sentiment_score_list(data)))
f.close()