LABR: A Large Scale Arabic Sentiment Analysis Benchmark

TL;DR

LABR is the largest Arabic sentiment analysis dataset with over 63,000 reviews, enabling improved research on sentiment classification and lexicon construction for the Arabic language.

Contribution

The paper introduces LABR, a large-scale Arabic sentiment dataset, along with comprehensive analysis, classification benchmarks, and a sentiment lexicon, advancing Arabic sentiment analysis research.

Findings

Dataset contains over 63,000 reviews with ratings.

Standard splits provided for balanced and unbalanced settings.

Constructed a sentiment lexicon from the dataset.

Abstract

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.