Question

我使用vader进行情感分析。当我在Vader词典中添加单个单词时，它可以正常工作，即它会根据我给该单词赋予的值将新添加的单词检测为正数或负数。代码如下：

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer 
sid_obj = SentimentIntensityAnalyzer() 
new_word = {'counterfeit':-2,'Good':2,}
sid_obj.lexicon.update(new_word)
sentence = "Company Caught Counterfeit." 
sentiment_dict = sid_obj.polarity_scores(sentence) 
tokenized_sentence = nltk.word_tokenize(sentence)
pos_word_list=[]
neu_word_list=[]
neg_word_list=[]

for word in tokenized_sentence:
    if (sid_obj.polarity_scores(word)['compound']) >= 0.1:
        pos_word_list.append(word)
    elif (sid_obj.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) 

print("Overall sentiment dictionary is : ", sentiment_dict) 
print("sentence was rated as ", sentiment_dict['neg']*100, "% Negative") 
print("sentence was rated as ", sentiment_dict['neu']*100, "% Neutral") 
print("sentence was rated as ", sentiment_dict['pos']*100, "% Positive") 

print("Sentence Overall Rated As", end = " ") 

# decide sentiment as positive, negative and neutral 
if sentiment_dict['compound'] >= 0.05 : 
    print("Positive") 

elif sentiment_dict['compound'] <= - 0.05 : 
    print("Negative") 

else : 
    print("Neutral")

输出如下：

Positive: []
Neutral: ['Company', 'Caught', '.']
Negative: ['Counterfeit']
Overall sentiment dictionary is :  {'neg': 0.6, 'neu': 0.4, 'pos': 0.0, 'compound': -0.4588}
sentence was rated as  60.0 % Negative
sentence was rated as  40.0 % Neutral
sentence was rated as  0.0 % Positive
Sentence Overall Rated As Negative

它非常适合词典中添加的一个单词。当我尝试通过使用以下代码添加多个单词来使用CSV文件执行相同操作时：我没有在Vader Lexicon中添加单词Counterfeit。

new_word={}
import csv
with open('Dictionary.csv', newline='') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        new_word[row['Word']] = int(row['Value'])
print(new_word)
sid_obj.lexicon.update(new_word)

以上代码的输出是一个字典，该字典已更新为词典。字典看起来像这样（它有大约2000个单词，但我只打印了几个单词）。它还包含伪造品作为一个单词：

{'CYBERATTACK': -2, 'CYBERATTACKS': -2, 'CYBERBULLYING': -2, 'CYBERCRIME': 
-2, 'CYBERCRIMES': -2, 'CYBERCRIMINAL': -2, 'CYBERCRIMINALS': -2, 
'MISCHARACTERIZATION': -2, 'MISCLASSIFICATIONS': -2, 'MISCLASSIFY': -2, 
'MISCOMMUNICATION': -2, 'MISPRICE': -2, 'MISPRICING': -2, 'STRICTLY': -2}

输出如下：

Positive: []
Neutral: ['Company', 'Caught', 'Counterfeit', '.']
Negative: []
Overall sentiment dictionary is :  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}
sentence was rated as  0.0 % Negative
sentence was rated as  100.0 % Neutral
sentence was rated as  0.0 % Positive
Sentence Overall Rated As Neutral

向词典中添加多个单词时，我哪里出错了？ CSV文件由两列组成。一个带有单词，另一个带有负或正数的值。为什么仍然被识别为中立？任何帮助将不胜感激。谢谢。

Answer 1

解决了，谢谢。问题是我将文本放在大写的字典中。通常应该将其存储为小写。字典词必须以小写形式存储。因为Vader在比较之前会将所有内容都转换为小写。

无法使用for循环在Vader词典中添加新单词。它可以完美地运行而无需循环。我该如何解决？

1 个答案: