无法更新VADER词典

Question

print(news['title'][5])级7.5级地震袭击秘鲁-厄瓜多尔边境地区-印度教

print(analyser.polarity_scores(news['title'][5])) {'neg'：0.0，'neu'：1.0，'pos'：0.0，‘复合’：0.0}

from nltk.tokenize import word_tokenize, RegexpTokenizer

import pandas as pd

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer


analyzer = SentimentIntensityAnalyzer()


sentence = news['title'][5]

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)
    elif (analyzer.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 = analyzer.polarity_scores(sentence)
print('\nScores:', score)

正面：[]中性：‘震级’，'7.5'，‘地震’，‘命中’，‘秘鲁-厄瓜多尔’，‘边境’，‘区域’，‘印度教’负面：[]

得分：{'neg'：0.0，'neu'：1.0，'pos'：0.0，‘复合’：0.0}

new_words = {
    'Peru-Ecuador': -2.0,
    'quake': -3.4,
}

analyser.lexicon.update(new_words)
print(analyzer.polarity_scores(sentence))

{'neg'：0.0，'neu'：1.0，'pos'：0.0，‘复合’：0.0}

from nltk.tokenize import word_tokenize, RegexpTokenizer

import pandas as pd

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer


analyzer = SentimentIntensityAnalyzer()


sentence = news['title'][5]

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)
    elif (analyzer.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 = analyzer.polarity_scores(sentence)
print('\nScores:', score)

正面：[]中性：‘震级’，'7.5'，‘地震’，‘命中’，‘秘鲁-厄瓜多尔’，‘边境’，‘区域’，‘印度教’负面：[]

得分：{'neg'：0.0，'neu'：1.0，'pos'：0.0，‘复合’：0.0}

Answer 1

您正在使用的代码是绝对好的。在更新字典时，使用的是analyser而不是analyzer (不确定为什么没有得到错误)。

new_words = {
    'Peru-Ecuador': -2.0,
    'quake': -3.4,
}
​
analyzer.lexicon.update(new_words)
print(analyzer.polarity_scores(sentence))

输出：

{'neg': 0.355, 'neu': 0.645, 'pos': 0.0, 'compound': -0.6597}

再一次提醒(不确定您是否犯了这个错误)。您不应该再次导入库。因为你更新过的文字都会消失。应采取以下步骤：

1. 导入库和字典
2. 更新字典(此步骤后不应再次导入库)
3. 计算情绪得分