如何为维达中的每个词典打印价分？

问题描述

我正在尝试使用 vader 打印句子中每个词典（单词）的效价分数，但我在这个过程中感到困惑。我能够使用维达将句子中的单词分为正面、负面和中性。我也想打印价分。如何解决这个问题？

sid = SentimentIntensityAnalyzer()
pos_word_list=[]
neu_word_list=[]
neg_word_list=[]

for word in tokenized_sentence:
    if (sid.polarity_scores(word)['compound']) >= 0.1:
        pos_word_list.append(word)
        sid.score_valence(word)
    elif (sid.polarity_scores(word)['compound']) <= -0.1:
        neg_word_list.append(word)
    else:
      neu_word_list.append(word)                

print('Positive:',pos_word_list)        
print('Neutral:',neu_word_list)    
print('Negative:',neg_word_list) 
score = sid.polarity_scores(sentence)
print('\nscores:',score)

这是我看到的代码 here。我希望它打印为

Positive: ['happy',1.3]
Neutral: ['paper','too','much',0]
Negative: ['missed',-1.2,'stupid',-1.9]

scores: {'neg': 0.491,'neu': 0.334,'pos': 0.175,'compound': -0.5848}

因此显示单词“happy”在句子中的效价得分为 1.3。

解决方法

如果您能提供您在代码中使用的句子，那就太好了。但是，我提供了一个句子，您可以将其替换为您的句子。

看看我的源代码：

import nltk
from nltk.tokenize import word_tokenize,RegexpTokenizer
from nltk.sentiment.vader import SentimentIntensityAnalyzer
 
Analyzer = SentimentIntensityAnalyzer()
 
sentence = 'Make sure you stay happy and less doubtful'
 
tokenized_sentence = nltk.word_tokenize(sentence)
pos_word_list=[]
neu_word_list=[]
neg_word_list=[]
 
for word in tokenized_sentence:
    if (Analyzer.polarity_scores(word)['compound']) >= 0.1:
        pos_word_list.append(word)
        pos_word_list.append(Analyzer.polarity_scores(word)['compound'])
    elif (Analyzer.polarity_scores(word)['compound']) <= -0.1:
        neg_word_list.append(word)
        neg_word_list.append(Analyzer.polarity_scores(word)['compound'])
    else:
        neu_word_list.append(word)
        neu_word_list.append(Analyzer.polarity_scores(word)['compound'])

print('Positive:',pos_word_list)
print('Neutral:',neu_word_list)
print('Negative:',neg_word_list) 
score = Analyzer.polarity_scores(sentence)
print('\nScores:',score)

从我从你的问题中了解到的，我猜你可能正在寻找这样的输出。如果没有，请告诉我。

输出：

Positive: ['sure',0.3182,'happy',0.5719]
Neutral: ['Make',0.0,'you','stay','and','less',0.0]
Negative: ['doubtful',-0.34]

Scores: {'neg': 0.161,'neu': 0.381,'pos': 0.458,'compound': 0.5984}