Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic
This addresses a specific problem in Arabic natural language processing for sentiment analysis applications, though it appears incremental in scope.
The authors tackled negation handling in sentiment classification for colloquial Arabic by proposing a rule-based algorithm, which improved classifier accuracy, precision, and recall compared to three baseline models.
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.