Amazon Review Polarity

Amazon Review Polaridy Dataset
Version 3, Updated 09/09/2015
ORIGIN
The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review. For more information, please refer to the following paper: J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. RecSys, 2013.
The Amazon reviews polarity dataset is constructed by Xiang Zhang ([e-mail]) from the above dataset. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

DESCRIPTION
The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. In the dataset, class 1 is the negative and class 2 is the positive. Each class has 1,800,000 training samples and 200,000 testing samples.
The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 or 2), review title and review text. The review title and text are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".

亚马逊评论极性数据集（Amazon Review Polarity Dataset），版本3，更新于2015年9月9日。

数据来源：
亚马逊评论数据集收录了亚马逊平台的用户评论，数据覆盖时长达18年，截至2013年3月共包含约3500万条评论。每条评论包含产品与用户信息、评分以及纯文本评论内容。如需获取更多信息，请参阅以下论文：J. McAuley 与 J. Leskovec 在2013年推荐系统大会（RecSys 2013）上发表的《隐藏因子与隐藏主题：基于评论文本理解评分维度》。
该亚马逊评论极性数据集由张翔（[e-mail]）基于上述原始数据集构建，已作为文本分类基准数据集应用于以下论文：Xiang Zhang、Junbo Zhao、Yann LeCun 在2015年神经信息处理系统进展（Advances in Neural Information Processing Systems 28，NIPS 2015）上发表的《字符级卷积网络用于文本分类》。

数据集详情：
亚马逊评论极性数据集通过将评分1、2的样本标记为负类，评分4、5的样本标记为正类构建，评分3的样本将被忽略。本数据集中，类别1对应负样本，类别2对应正样本。每个类别分别包含180万条训练样本与20万条测试样本。
训练集与测试集分别存储于train.csv与test.csv文件中，文件采用逗号分隔值（Comma-Separated Values，CSV）格式，共包含3列，依次对应类别索引（1或2）、评论标题与评论正文。评论标题及正文使用双引号进行转义，若内容内部出现双引号，则通过两个连续双引号（""）进行转义；换行符则通过反斜杠加小写字母n（
）实现转义。