计算 Sentiwordnet 的极性

Question

我正在尝试计算以比较评论的分数（从 1 到 5）与从文本评论的情感分析中提取的分数。我正在使用 sentiwordnet，我设法获得了正面、负面和 objective 分数，但我无法迭代 42 条评论的每个句子中每个单词的分数来计算分数。

这是我的代码：

def preprocess_token(text): 

    lower_text=text.lower() 
    tokens = nltk.tokenize.word_tokenize(lower_text)
    return tokens

def penn_to_wn(tag):

    if tag.startswith('J'):
        return wn.ADJ
    elif tag.startswith('N'):
        return wn.NOUN
    elif tag.startswith('R'):
        return wn.ADV
    elif tag.startswith('V'):
        return wn.VERB
    return None

  lemmatizer = WordNetLemmatizer()

def get_sentiment(word,tag):
    
    wn_tag = penn_to_wn(tag)
    if wn_tag not in (wn.NOUN, wn.ADJ, wn.ADV):
        return []

    lemma = lemmatizer.lemmatize(word, pos=wn_tag)
    if not lemma:
        return []

    synsets = wn.synsets(word, pos=wn_tag)
    if not synsets:
        return []

    synset = synsets[0]
    swn_synset = swn.senti_synset(synset.name())

    return [swn_synset.pos_score(),swn_synset.neg_score(),swn_synset.obj_score()]


data["text_n"] = data['Text'].str.replace('[^\w\s]','')

data['tokens']=data['text_n'].apply(preprocess_token)


word_list=[]

for i in range(len(data['tokens'])):

    word_list.append([])

for i in range(len(data['tokens'])): 

    for word in data['tokens'][i]:

        if word[:].isalpha():

            word_list[i].append(word[:])



tagged_tokens=[]

for token in word_list:

    tagged_tokens.append(nltk.pos_tag(token))
    


senti_val=[]

for i in range(len(tagged_tokens)):

    t=tuple(get_sentiment(x,y) for x,y in tagged_tokens[i])

    values= [x for x in t if x]

    senti_val.append(values)

data['value']=senti_val

calc=[]
for i in range(len(senti_val)):
    r=senti_val[i][i][0] - senti_val[i][i][1]
    calc.append(r)
#This last chunk of code gives me error:
Traceback (most recent call last):

  File "C:\Users\----\OneDrive\python\reviews.py", line 158, in <module>
    r=senti_val[i][i][0] - senti_val[i][i][1]

IndexError: list index out of range

#I want to get the result of subtracting the negative score from the positive score for each word of each sentence and get it in a list.

任何关于问题所在或我下一步应该做什么的建议都将不胜感激。

提前致谢

Answer 1

calc=[]
#iterating over each sentence 
for i in range(len(senti_val)):
    # subtract positive and negative score of each token in the sentence 
    scores = [token_score[0] - token_score[1] for token_score in senti_val[i]]
    # if you want to sum up all the score within the sentence
    # scores = sum(scores) 
    print(scores)
    calc.append(scores)

计算 Sentiwordnet 的极性

Calculate the Polarity of Sentiwordnet

python

wordnet

sentiment-analysis

senti-wordnet