Python情感分析中的字符串列表处理

Question

我喜欢做一个关于酒店评论的小情绪分析。你知道吗

示例（stop\u word\u filter:

[“偶数”、“尽管”、“图片”、“显示”、“干净”、“房间”、“实际”、“房间”、“退出”、“脏”、“过期”、“同时”、“检查”、“15”、“时钟”、“房间”、“未”、“准备好”、“时间”]

我发现这是关于“旅馆”和“干净”的。我想把“否定”和“干净”联系起来，后者应该是“脏的”。 我整合了一个否定词列表。 我正在等待执行。。。你知道吗

代码：

bullets = [] #output
distances = []
bad_word_locations = []
rubrik_word_location = [] #category_word

#if there is a category word in the review

if len(rubrik_uR_list) == 1:

    #get the index of that one 
        rubrik_word_location = stop_words_filter.index(rubrik_uR_list[0])

        #go throu all negative words and if one of them in my sentence get the index of that word
        for w in negativ_words_list:  
            if w in stop_words_filter:
                bad_word_locations.append(stop_words_filter.index(w)) 

            #NOW ITS GETTING CRUCIAL 
            #if we found one 
            if len(bad_word_locations) > 0:

                #I need to some how catch now the closest word, my code is not doing this
                distances.append(abs(rubrik_word_location-bad_word_locations[0])) 

                bullets.append(stop_words_filter[min(distances)])

                #if I got more categories in one review I need to remind that somehow...
                blacklist.append(stop_words_filter[min(distances)])

我明白了，我编程很差。 我将衷心感谢你的帮助。你知道吗

提前谢谢你，尼克拉斯

Answer 1

我自己就知道了。你知道吗

def getBulletPoint(pos_filter,stop_words_filter, rubrik_uR_list):      

    index_of_category_word = 0
    distance = 0
    count = 0 

    #Finde alle negaive Wörter 
    for w in negativ_words_list:             
        if w in stop_words_filter:
            bad_words.append(w) 

    #finde die Indexe der negativen Wörter        
    for w in bad_words:
        word_index.append(stop_words_filter.index(w))

    if len(rubrik_uR_list) > 0 and len(bad_words) > 0 : #Wenn wir überhaupt ein Rubrikwort haben



#      -Loop         -                
        for w in rubrik_uR_list:     

            saved_distance = 1000 

#            bullets.append(rubrik_list[count])
#            bullets.append(rubrik_uR_list[count])

            index_of_category_word = stop_words_filter.index(rubrik_uR_list[count])

            for i in word_index:
                distance = abs(i-index_of_category_word)

                if distance < saved_distance:
                    current_bullet = (stop_words_filter[i])
                    saved_distance = distance

            bullets.append(current_bullet)

            count = count + 1

祝你好运，尼克拉斯