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 jieba
import 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 = 0
negcount2 = 0
negcount3 = 0
        for word in segtmp:
            if word in posdict:  # 判断词语是否是情感词
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  # 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 += 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 or negdict:
poscount3 += 2
negcount3 += 2
                        break
i += 1 # 扫描词位置前移
 
 
            # 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 = -poscount3
pos_count = -negcount3
            else:
pos_count = poscount3
neg_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)))