情感分析

相关数据集
–就是判断一句话的词性，可以从几个方面来看
1、分词，将一句话分成几个词语 不同的分词方法会产生不同的效果，利用jieba分词的默认形式（即每个字在词语中会且仅会出现一次）
2、判断词语的词性，如情感词，反义词，程度副词 。并用数字标注词语的性质[word,positon,score]
–反义词可以直接算为-1
–jieba分词出的情感词和情感词库不对应。情感词库太差啦
3、计算分数，情感词语之间求和。不同词语组合求积

# -*- coding: utf-8 -*-"""Created on Wed May  3 16:25:05 2017http://www.jianshu.com/p/4cfcf1610a73?nomobile=yes 参考链接#情感分析@author: chuc"""from collections import defaultdictimport jieba"""1. 文本切割"""def sent2word(sentence):    """    Segment a sentence to words    Delete stopwords    """    jieba.load_userdict("motion/dict.txt")    segList = jieba.cut(sentence)    segResult = []    for w in segList:        segResult.append(w)    '''f = open('motion/stopword.txt')    stopwords = f.readlines()    f.close()    newSent = []    for word in segResult:        if word in stopwords:            # print "stopword: %s" % word            continue        else:            newSent.append(word)'''    return segResult"""2. 情感定位"""def classifyWords(wordDict):    # (1) 情感词    f=open('motion/BosonNLP_sentiment_score.txt',encoding='utf-8')    senList = f.readline()    senDict = defaultdict()    while senList:        #senDict.append(senList.split())        senDict[senList.split(' ')[0]] = senList.split(' ')[1]        senList = f.readline()    f.close()    # (2) 否定词    g = open('motion/notDict.txt',encoding='utf-8')    notList = g.readline()    notDic = []    while notList:        notDic.append(notList)        notList = g.readline()    g.close()        # (3) 程度副词    f = open('motion/degree.txt')    degreeList = f.readline()    degreeDict = defaultdict()    while degreeList:        degreeDict[degreeList.split()[0]] = degreeList.split()[1]        degreeList = f.readline()    f.close()    senWord = defaultdict()    notWord = defaultdict()    degreeWord = defaultdict()    t = 0    for word in wordDict:        print(word)        if word in senDict.keys() and word not in notDic and word not in degreeDict.keys():            senWord[t] = senDict[word]        elif word in notDic[0] and word not in degreeDict.keys():            notWord[t] = -1        elif word in degreeDict.keys():            degreeWord[t] = degreeDict[word]        t = t+1    #print( senWord, notWord, degreeWord)    return senWord, notWord, degreeWord'''计算句子分数  '''def score(sen,no,degree,word):    score = 0      for i in range(len(word)):        if  i in no.keys() and i+1 in sen.keys():            sen[i+1] =float(no[i])*float(sen[i+1])        elif i in degree.keys() and i+1 in no.keys() and i+2 in sen.keys():            sen[i+2] =float(no[i])*float(sen[i+2]*float(degree[i]))        elif i in degree.keys() and i+1 in sen.keys():            sen[i+1] = float(degree[i])*float(sen[i+1])        elif i in degree.keys() and i+1 in degree.keys():            sen[i]=float(degree[i])*float(degree[i+1])   #考虑不同的短语组合算分            for j in sen.keys():        score = score+float(sen[j])            return scoredef culate(sentences):    sp = sent2word(sentences)    d,dd,ddd = classifyWords(sp)    score1 = score(d,dd,ddd,sp)    return score1

测试的结果不是很好。
1、句子的组合未写完全
2、结巴分词库和分类情感的词库不对应导致像（“很差”不在情感词库中，导致计算出的得分为0）
3、得分范围广，可以在训练后，归一化处理