Amazon商品评论数据集含NLP Python程序代码电子商务推荐系统情感分析经典

（600多MB的数据文件压缩包）
Amazon Review Dataset数据集记录了用户对亚马逊网站商品的评价，是推荐系统的经典数据集、




该数据集由数百万个Amazon客户评论（输入文本）和星级（输出标签）组成，用于学习如何训练fastText进行情感分析。这里的想法是数据集不仅仅是玩具-合理规模的真实业务数据-但可以在适度的笔记本电脑上在几分钟内进行训练。



Amazon数据集可以根据商品类别分为 Books，Electronics，Movies and TV，CDs and Vinyl等子数据集，这些子数据集包含两类信息：   商品信息描述asin        商品idtitle        商品名称price        价格imUrl        商品图片链接related        相关商品salesRank        折扣信息brand        品牌categories        目录类别官方例子：{"asin": "0000031852","title": "Girls Ballet Tutu Zebra Hot Pink","price": 3.17,"imUrl": "http://ecx.images-amazon.com/images/I/51fAmVkTbyL._SY300_.jpg","related":{ "also_bought": ["B00JHONN1S", "B002BZX8Z6"], "also_viewed": ["B002BZX8Z6", "B00JHONN1S"], "bought_together": ["B002BZX8Z6"]},"salesRank": {"Toys & Games": 211836},"brand": "Coxlures","categories": [["Sports & Outdoors", "Other Sports", "Dance"]]}123456789101112131415用户评分记录数据reviewerID        用户idasin        商品idreviewerName        用户名helpful        有效评价率（helpfulness rating of the review, e.g. 2/3）reviewText        评价文本overall        评分summary        评价总结unixReviewTime        评价时间戳reviewTime        评价时间{  "reviewerID": "A2SUAM1J3GNN3B",  "asin": "0000013714",  "reviewerName": "J. McDonald",  "helpful": [2, 3],  "reviewText": "I bought this for my husband who plays the piano.  He is having a wonderful time playing these old hymns.  The music  is at times hard to read because we think the book was published for singing from more than playing from.  Great purchase though!",  "overall": 5.0,  "summary": "Heavenly Highway Hymns",  "unixReviewTime": 1252800000,  "reviewTime": "09 13, 2009"}1234567891011Amazon数据集读取：因为下载的数据是json文件，不易操作，这里主要介绍如何将json文件转化为csv格式文件。以2014版Amazon Electronics数据集的转化为例：商品信息读取import pickleimport pandas as pdfile_path = 'meta_Electronics.json'fin = open(file_path, 'r')df = {}useless_col = ['imUrl','salesRank','related','title','description']  # 不想要的字段i = 0for line in fin:    d = eval(line)    for s in useless_col:        if s in d:            d.pop(s)    df = d     i += 1df = pd.DataFrame.from_dict(df, orient='index')df.to_csv('meta_Electronics.csv',index=False)123456789101112131415161718用户评分记录数据读取file_path = 'Electronics_10.json'fin = open(file_path, 'r')df = {}useless_col = ['reviewerName','reviewText','unixReviewTime','summary'] # 不想要的字段i = 0for line in fin:    d = eval(line)    for s in useless_col:        if s in d:            d.pop(s)    df = d     i += 1df = pd.DataFrame.from_dict(df, orient='index')df.to_csv('Electronics_10.csv',index=False)