计算情绪

Question

我正在使用python NLTK对twitter数据进行情绪分析。我需要一个包含+ ve和-ve极性单词的字典。我已经阅读了很多关于sentiwordnet的内容，但是当我将它用于我的项目时，它并没有提供有效和快速的结果。我想我没有正确使用它。谁能告诉我使用它的正确方法？以下是我到目前为止所做的步骤：

推文的标记化
令牌的POS标记
将每个标签传递给sentinet

我正在使用nltk包进行标记化和标记。请参阅下面的代码的一部分：

import nltk
from nltk.stem import *
from nltk.corpus import sentiwordnet as swn

tokens=nltk.word_tokenize(row) #for tokenization, row is line of a file in which tweets are saved.
tagged=nltk.pos_tag(tokens) #for POSTagging

for i in range(0,len(tagged)):
     if 'NN' in tagged[i][1] and len(swn.senti_synsets(tagged[i][0],'n'))>0:
            pscore+=(list(swn.senti_synsets(tagged[i][0],'n'))[0]).pos_score() #positive score of a word
            nscore+=(list(swn.senti_synsets(tagged[i][0],'n'))[0]).neg_score()  #negative score of a word
    elif 'VB' in tagged[i][1] and len(swn.senti_synsets(tagged[i][0],'v'))>0:
           pscore+=(list(swn.senti_synsets(tagged[i][0],'v'))[0]).pos_score()
           nscore+=(list(swn.senti_synsets(tagged[i][0],'v'))[0]).neg_score()
    elif 'JJ' in tagged[i][1] and len(swn.senti_synsets(tagged[i][0],'a'))>0:
           pscore+=(list(swn.senti_synsets(tagged[i][0],'a'))[0]).pos_score()
           nscore+=(list(swn.senti_synsets(tagged[i][0],'a'))[0]).neg_score()
    elif 'RB' in tagged[i][1] and len(swn.senti_synsets(tagged[i][0],'r'))>0:
           pscore+=(list(swn.senti_synsets(tagged[i][0],'r'))[0]).pos_score()
           nscore+=(list(swn.senti_synsets(tagged[i][0],'r'))[0]).neg_score()

最后，我将计算有多少推文是正面的，有多少推文是否定的。我哪里错了？我该怎么用？还有其他类似的字典易于使用吗？

Answer 1

是的，您可以使用其他词典。你可以在这里找到一小部分词典：http://sentiment.christopherpotts.net/lexicons.html#resources 看来刘冰的意见词典很容易使用。

除了链接到那些词典之外，网站是关于情绪分析的非常好的教程。

Answer 2

计算情绪

alist = [all_tokens_in_doc]

totalScore = 0

count_words_included = 0

for word in all_words_in_comment:

    synset_forms = list(swn.senti_synsets(word[0], word[1]))

    if not synset_forms:

        continue

    synset = synset_forms[0] 

    totalScore = totalScore + synset.pos_score() - synset.neg_score()

    count_words_included = count_words_included +1

final_dec = ''

if count_words_included == 0:

    final_dec = 'N/A'

elif totalScore == 0:

    final_dec = 'Neu'        

elif totalScore/count_words_included < 0:

    final_dec = 'Neg'

elif totalScore/count_words_included > 0:

    final_dec = 'Pos'

return final_dec

使用nltk Sentiwordnet和python

2 个答案:

计算情绪