NLP之情感分析：基于python编程(jieba库)实现中文文本情感分析(得到的是情感评分)
输出结果
1、测试对象
data1= '今天上海的天气真好！我的心情非常高兴！如果去旅游的话我会非常兴奋！和你一起去旅游我会更加幸福！'
data2= '今天上海天气真差,非常讨厌下雨,把我冻坏了,心情太不高兴了,不高兴,我真的很生气！'
data3= '美国华裔科学家,祖籍江苏扬州市高邮县,生于上海,斯坦福大学物理系,电子工程系和应用物理系终身教授!'
2、输出结果
很明显，data1情感更加积极！data2情感消极！data3情感中等！
[[240.0, 104.0, 8.3, 3.6, 8.0, 2.4]]
[[0.0, 134.0, 0.0, 4.8, 0.0, 3.2]]
[[2, 66, 0.1, 3.3, 0.4, 1.7]]
[[2, 2, 0.1, 0.1, 0.4, 0.4]]
设计思路
后期更新……
使用的相关文件
主要部分代码实现
import jiebaimport numpy as np …… def sentiment_score_list(dataset):seg_sentence = dataset.split('。') count1 = []count2 = [] for sen in seg_sentence: #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 #积极词反转后的分值poscount3 = 0 #积极词的最后分值（包括叹号的分值）negcount = 0negcount2 = 0negcount3 = 0 for word in segtmp: if word in posdict: # 判断词语是否是情感词poscount += 1c = 0for w in segtmp[a:i]: # 扫描情感词前的程度词if w in mostdict:poscount *= 4.0elif w in verydict:poscount *= 3.0elif w in moredict:poscount *= 2.0elif w in ishdict:poscount *= 0.5elif w in deny_word:c += 1if judgeodd(c) == 'odd': # 扫描情感词前的否定词数poscount *= -1.0poscount2 += poscountposcount = 0poscount3 = poscount + poscount2 + poscount3poscount2 = 0else:poscount3 = poscount + poscount2 + poscount3poscount = 0a = i + 1 # 情感词的位置变化 elif word in negdict: # 消极情感的分析，与上面一致negcount += 1d = 0for w in segtmp[a:i]:if w in mostdict:negcount *= 4.0elif w in verydict:negcount *= 3.0elif w in moredict:negcount *= 2.0elif w in ishdict:negcount *= 0.5elif w in degree_word:d += 1if judgeodd(d) == 'odd':negcount *= -1.0negcount2 += negcountnegcount = 0negcount3 = negcount + negcount2 + negcount3negcount2 = 0else:negcount3 = negcount + negcount2 + negcount3negcount = 0a = 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 or negdict:poscount3 += 2negcount3 += 2 breaki += 1 # 扫描词位置前移 # 以下是防止出现负数的情况pos_count = 0neg_count = 0 if poscount3 < 0 and negcount3 > 0:neg_count += negcount3 - poscount3pos_count = 0 elif negcount3 < 0 and poscount3 > 0:pos_count = poscount3 - negcount3neg_count = 0 elif poscount3 < 0 and negcount3 < 0:neg_count = -poscount3pos_count = -negcount3 else:pos_count = poscount3neg_count = negcount3 count1.append([pos_count, neg_count])count2.append(count1)count1 = []return count2 def sentiment_score(senti_score_list):score = [] for review in senti_score_list:score_array = np.array(review)Pos = np.sum(score_array[:, 0])Neg = np.sum(score_array[:, 1])AvgPos = np.mean(score_array[:, 0])AvgPos = float('%.1f'%AvgPos)AvgNeg = np.mean(score_array[:, 1])AvgNeg = float('%.1f'%AvgNeg)StdPos = np.std(score_array[:, 0])StdPos = float('%.1f'%StdPos)StdNeg = np.std(score_array[:, 1])StdNeg = float('%.1f'%StdNeg)score.append([Pos, Neg, AvgPos, AvgNeg, StdPos, StdNeg]) return score data1= '今天上海的天气真好！我的心情非常高兴！如果去旅游的话我会非常兴奋！和你一起去旅游我会更加幸福！'data2= '今天上海天气真差,非常讨厌下雨,把我冻坏了,心情太不高兴了,不高兴,我真的很生气！'data3= '美国华裔科学家,祖籍江苏扬州市高邮县,生于上海,斯坦福大学物理系,电子工程系和应用物理系终身教授!'print(sentiment_score(sentiment_score_list(data1)))print(sentiment_score(sentiment_score_list(data2)))print(sentiment_score(sentiment_score_list(data3)))
