Multilevel sentiment analysis in arabic

TL;DR

This paper enhances Arabic sentiment analysis by identifying optimal machine learning methods and features, achieving high F-scores with neural networks at both term and document levels.

Contribution

It investigates the best classifier and feature vectors for Arabic sentiment analysis and introduces rules for negation and intensification handling.

Findings

Neural Network classifier performs best.

F-score of 0.92 at term level.

F-score of 0.94 and 0.93 at document level.

Abstract

In this study, we aimed to improve the performance results of Arabic sentiment analysis. This can be achieved by investigating the most successful machine learning method and the most useful feature vector to classify sentiments in both term and document levels into two (positive or negative) categories. Moreover, specification of one polarity degree for the term that has more than one is investigated. Also to handle the negations and intensifications, some rules are developed. According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language. Furthermore, the average F-score achieved in the term level SA for both positive and negative testing classes is 0.92. In the document level SA, the average F-score for positive testing classes is 0.94, while for negative classes is 0.93.