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我正在尝试使用 vader 在句子中打印每个词典（单词）的价分数，但我在这个过程中感到困惑。我可以使用 vader 将句子中的单词分类为积极、消极和中性。我也想打印价分数。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)

这是我在这里看到的代码。我希望它打印为

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

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

因此在句子中显示“快乐”这个词有 1.3 的化合价分数。

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1 回答 1

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如果您能提供您用于代码的句子，那就太好了。但是，我提供了一个句子，您可以用您的句子替换它。

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)

根据我从您的问题中了解到的情况，我猜您可能正在寻找这样的输出。让我知道，否则。

输出：

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

Scores: {'neg': 0.161, 'neu': 0.381, 'pos': 0.458, 'compound': 0.5984}
于 2021-01-24T17:34:21.997 回答