SentiArabic: A Sentiment Analyzer for Standard Arabic

Abstract

Sentiment analysis has been receiving increasing interest as it conveys valuable information in regard to people’s preferences and opinions. In this work, we present a sentiment analyzer that identifies the overall contextual polarity for Standard Arabic text. The contribution of this work is threefold. First, we modify and extend SLSA; a large-scale Sentiment Lexicon for Standard Arabic. Second, we build a sentiment corpus of Standard Arabic text tagged for its contextual polarity. This corpus represents the training, development and test sets for the proposed system. Third, we build a lightweight lexicon-based sentiment analyzer for Standard Arabic (SentiArabic). The analyzer does not require running heavy computations, where the link to the lexicon is carried out through a morphological lookup as opposed to conducting a rich morphological analysis, while the assignment of the sentiment is based on a simple decision tree that uses polarity scores as opposed to a more complex machine learning approach that relies on lexical information, while negation receives special handling. The analyzer is highly efficient as it achieves an F-score of 76.5% when evaluated on a blind test set, which is the highest results reported for that set, and an absolute 3.0% increase over a state-of-the-art system that uses deep-learning models.