Amazon movie reviews

Secondary Data. Primary Data could be found here: https://snap.stanford.edu/data/web-Movies.html File allReviews.csv consists of 7911684 movie reviews from amazon. It has removed the profile name of the reviewer, the review-summary, and the review-text from the primary data. The data span a period of more than 10 years, including all up to October 2012. Each row contains 6 fields: 1. Product ID (e.g. B003AI2VGA) 2. User ID (e.g. A141HP4LYPWMSR) 3. Count of thumb-ups received by this review (e.g. 7) 4. Total thumb count of this review (sum of thumb-ups and thumb-downs, e.g. 7) 5. Given rating in a discrete likert scale of 1 to 5(e.g. 3) 6. Time of the review (unix time: e.g. 1182729600) For example, a sample row from this file is: B003AI2VGA,A1I7QGUDP043DG,8,10,5,1164844800 File Reviewers50plus.csv, contains the user ID of all (16341) the reviewers with more than 50 reviews each. File MovieID177k.csv, contains the product ID of all (177111) the movies reviewed by the reviewers with more than 50 reviews. File Set2userid2000.csv, contains the user ID of 2000 reviewers who have the largest thumb-up to the thumb-down difference from Reviewers50plus.csv. The four files in "Product Ratings" folder contains “product ratings” of all the movies from MovieID177k.csv derived using 4 different techniques. Each file consists of 2 columns: product ID and product rating. The 9 files in "Recommended Experts" folder contains 37 different sets of “recommended expert reviewers”. Each file contains 200 rows of user IDs. Primary data citation: "McAuley, J. J., & Leskovec, J. (2013, May). From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In Proceedings of the 22nd international conference on World Wide Web (pp. 897-908)."本数据集为二级数据集。原始数据集可通过以下链接获取：https://snap.stanford.edu/data/web-Movies.html。文件allReviews.csv包含来自亚马逊的7911684条电影评论，已从原始数据中移除评论者的个人资料名称、评论摘要与评论正文。该数据覆盖时长超过10年，涵盖截至2012年10月的全部相关评论。每行包含6个字段： 1. 商品ID（Product ID）：示例为B003AI2VGA 2. 用户ID（User ID）：示例为A141HP4LYPWMSR 3. 该评论获得的点赞数（Count of thumb-ups）：示例为7 4. 该评论的总投票数（Total thumb count，为点赞数与点踩数之和）：示例为7 5. 以1至5分离散李克特量表（Likert scale）给出的评分：示例为3 6. 评论发布时间（Unix时间戳，Unix time）：示例为1182729600 该文件的示例行如下：B003AI2VGA,A1I7QGUDP043DG,8,10,5,1164844800 文件Reviewers50plus.csv包含所有16341位评论数超过50条的评论者的用户ID。 文件MovieID177k.csv包含所有由上述评论数超过50条的评论者所评论的177111部电影的商品ID。 文件Set2userid2000.csv包含从Reviewers50plus.csv中筛选出的2000位点赞数与点踩数差值最高的评论者的用户ID。 "Product Ratings"文件夹内的4个文件，包含基于4种不同技术从MovieID177k.csv中的电影导出的全部商品评分。每个文件包含两列：商品ID与商品评分。 "Recommended Experts"文件夹内的9个文件，共包含37组推荐专家评论者。每个文件包含200行用户ID。 原始数据引用：McAuley, J. J. 与 Leskovec, J. (2013年5月)。《从爱好者到鉴赏家：通过在线评论建模用户专业度的演化过程》，收录于第22届国际万维网大会论文集（第897-908页）。