nltp APP-分析买家评论的评分-高频词(二维关系)

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# -*- coding: utf-8 -*- from nltkimport *# TO FIX : No such file or directory os.chdir(r‘E:\zpy‘)f = open(‘reviews_text_lt_3.txt‘, ‘r‘) f_r = f.read() strList = f_r.split(‘ ‘) fdist1 = FreqDist(strList) #总的词数 print fdist1 #表达式 keys()为我们提供了文本中所有不同类型的链表 vocabulary1 = fdist1.keys() #通过切片看看这个链表的前 50 项 res0_50 =vocabulary1[:50] print res0_50

 
 
C:\> python E:\zpy\wltp.py < FreqDist with 16789 samples and 180043 outcomes> [‘‘, ‘raining‘, ‘disappointing.It‘, ‘uncomfortable...‘, "lot‘s", ‘uv.\nSo,‘, ‘yellow‘, ‘Seller‘, ‘four‘, ‘vaporizers.I‘, ‘Does‘, ‘completely!!‘, ‘hanging‘, ‘Monday,‘, ‘asap!!This‘, ‘Until‘, ‘instead.The‘, ‘malfunctioned.‘, ‘Lately‘, ‘looking‘, ‘LAST‘, ‘eligible‘, ‘electricity‘, ‘DISAPPOINTED‘, ‘oneWorks‘, ‘powdery‘, ‘unanswered‘, ‘also.‘, ‘refun ‘sooooo‘, ‘foul‘, ‘on\nafter‘, ‘fingers.‘, ‘advice:‘, ‘fingers,‘, ‘advice?‘, ‘each),‘, ‘month.I‘]C:\>

 
 
SELECT amz_review_text FROM amz_reviews_grab_us WHERE amz_review_rating < 3 LIMIT 3000;

 
对于通过亚马逊us美国站的买家而言,在数据库前3000条的时间周期y-m-d内,在不考虑品类、价格、评分相对值等因素的情况下,
【nltp APP-分析买家评论的评分-高频词(二维关系)】暂得出以下推测:
0-卖品属性为yellow,其他条件相同情况下,可能不受欢迎,评分相对低;
1-周一可能会给买家糟糕的购买体验,周一的促销活动须结合其他因素,如人文风俗、新闻事件慎重;
注:dev的当前视角
 




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