We introduce LABR, the largest sentiment analysis dataset to-date for the
Arabic language. It consists of over 63,000 book reviews, each rated on a scale
of 1 to 5 stars. We investigate the properties of the dataset, and present its
statistics. We explore using the dataset for two tasks: (1) sentiment polarity
classification; and (2) ratings classification. Moreover, we provide standard
splits of the dataset into training, validation and testing, for both polarity
and ratings classification, in both balanced and unbalanced settings. We extend
our previous work by performing a comprehensive analysis on the dataset. In
particular, we perform an extended survey of the different classifiers
typically used for the sentiment polarity classification problem. We also
construct a sentiment lexicon from the dataset that contains both single and
compound sentiment words and we explore its effectiveness. We make the dataset
and experimental details publicly available.

介绍 LABR 数据集，这是迄今为止最大的用于阿拉伯语情感分析的数据集，包括超过 63,000 本书的评论，探讨数据集的性质和使用该数据集进行情感倾向和评分分类的任务。此外，提供数据集的标准分割，用于训练，验证和测试情感倾向和评分分类等。