使用Vader库进行情感分析

Question

下面是我用来使用Vader Library进行情感分析的Python代码。我使用了多个for循环来获得积极，消极，中立和复合的情绪评分。

如何使用一个for循环获得积极，消极，中立和复合的得分？

        import nltk
        import pandas as pd
        from nltk.sentiment.vader import SentimentIntensityAnalyzer
        sid = SentimentIntensityAnalyzer()
        remarks = pd.read_csv('/Users/XXXXXX/Desktop/comments/KCComments.txt', 
                  sep='\t')
        df = pd.DataFrame(remarks, columns= ['Overall Satisfaction',
              Experience','Category','Reviews'])
        remarks.head(5)
        def get_sentiment1(row, **kwargs):
        sentiment_score = sid.polarity_scores(str(row))
        positive_meter = round((sentiment_score['pos']*100),2)
        negative_meter = round((sentiment_score['neg']*100),2)
        return positive_meter if kwargs['k'] == 'positive' else negative_meter

        df['positive'] = df.Reviews.apply(get_sentiment1, k='positive')
        df['negative'] = df.Reviews.apply(get_sentiment1, k='negative')

        for index, row in df.iterrows(): 
         print(row['Overall Satisfaction Experience'], 
                         row['Category'],row['Reviews'],"Positive : 
                           {}, Negative : {}".format(row['positive'], 
                           row['negative']))

        def get_sentiment2(row, **kwargs):
        sentiment_score = sid.polarity_scores(str(row))
        positive_meter = round((sentiment_score['pos']*100),2)
        neutral_meter = round((sentiment_score['neu']*100),2)
        return positive_meter if kwargs['k'] == 'positive' else neutral_meter

        df['positive'] = df.Reviews.apply(get_sentiment2, k='positive')
        df['neutral'] = df.Reviews.apply(get_sentiment2, k='neutral')
        for index, row in df.iterrows(): 
           print(row['Overall Satisfaction Experience'], 
         row['Category'],row['Reviews'],"Positive : 
           {}, Negative : {}, Neutral: {}".format(row['positive'], 
          row['negative'],row['neutral']))

        def get_sentiment3(row, **kwargs):
        sentiment_score = sid.polarity_scores(str(row))
        positive_meter = round((sentiment_score['pos']*100),2)
        compound_meter = round((sentiment_score['compound']*100),2)
        return positive_meter if kwargs['k'] == 'positive' else compound_meter

        df['positive'] = df.Reviews.apply(get_sentiment3, k='positive')
        df['compound'] = df.Reviews.apply(get_sentiment3, k='compound')

        for index, row in df.iterrows(): 
         print(index,row['Overall Satisfaction Experience'], 
         row['Category'],row['Reviews'],"Positive : {}, Negative : {}, Neutral: 
                {}, compound: {}".format(row['positive'], 
                  row['negative'],row['neutral'],row['compound']))
        df.to_csv('Comments_modifiedzz.csv')  
        df.head(16)