Amazon Review Polarity

<b>Amazon Review Polaridy Dataset</b><br>Version 3, Updated 09/09/2015<br>ORIGIN<br>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.<br>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).<br><br>DESCRIPTION<br>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.<br>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".<br>

### 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

#### 数据集来源
本亚马逊评论原始数据集收录了亚马逊平台的用户评论，时间跨度达18年，截至2013年3月共包含约3500万条评论。每条评论涵盖产品与用户信息、评分以及纯文本评论内容，详细信息可参考以下论文：J. McAuley 与 J. Leskovec. 隐藏因子与隐藏主题：基于评论文本理解评分维度. RecSys, 2013.

该亚马逊评论极性数据集由Xiang Zhang（[e-mail]）基于上述原始数据集构建，并在以下论文中被用作文本分类基准数据集：Xiang Zhang、Junbo Zhao、Yann LeCun. 面向文本分类的字符级卷积网络. 《神经信息处理系统进展》28（NIPS 2015）.

#### 数据集说明
本数据集的标注规则为：将评分1和2的样本标记为负向评论，评分4和5的样本标记为正向评论，评分3的样本被剔除。数据集中类别1代表负向评论，类别2代表正向评论。每个类别包含180万条训练样本与20万条测试样本。

训练集文件train.csv与测试集文件test.csv采用逗号分隔值（Comma-Separated Values，CSV）格式存储全部样本，每份文件包含3列，分别对应类别索引（1或2）、评论标题与评论文本。评论标题与文本使用双引号进行转义，内部出现的双引号需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
进行转义。