Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

TL;DR

This paper presents a rule-based negation handling algorithm for colloquial Arabic sentiment analysis, improving machine learning classifier accuracy, precision, and recall by addressing negation effects.

Contribution

It introduces a simple, linguistically-informed negation handling method tailored for colloquial Arabic sentiment classification, enhancing classifier performance.

Findings

Improved accuracy, precision, and recall with the proposed negation handling algorithm.

Positive impact on multiple machine learning classifiers.

Comparison with baseline models demonstrates effectiveness.

Abstract

One crucial aspect of sentiment analysis is negation handling, where the occurrence of negation can flip the sentiment of a sentence and negatively affects the machine learning-based sentiment classification. The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for colloquial Arabic. In this paper, the author addresses the negation problem of machine learning-based sentiment classification for a colloquial Arabic language. To this end, we propose a simple rule-based algorithm for handling the problem; the rules were crafted based on observing many cases of negation. Additionally, simple linguistic knowledge and sentiment lexicon are used for this purpose. The author also examines the impact of the proposed algorithm on the performance of different machine learning algorithms. The results given by the proposed algorithm are compared with three baseline models. The experimental results show that there is a positive impact on the classifiers accuracy, precision and recall when the proposed algorithm is used compared to the baselines.