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 2017 http://www.jianshu.com/p/4cfcf1610a73?nomobile=yes 参考链接 #情感分析 @author: chuc """ from collections import defaultdict import 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 score def 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
