Sentiment Analysis For Jordanian Dialect Using Memetic Algorithm And Support Vector Machine Classifier

Abstract

Sentiment analysis (SA) is a vital text-mining task for extracting and classifying opinions from textual data as positive, negative, or neutral. This research investigates sentiment analysis for the Jordanian Arabic dialect by integrating a Memetic Algorithm (MA) for feature selection with three machine learning classifiers: Support Vector Machine (SVM), Multinomial Naïve Bayes (MultinomialNB), and Logistic Regression (LR). The core strategy of the MA is to combine global and local search to filter out irrelevant features and select the most relevant ones, thereby optimising the feature space for classification. The model's accuracy served as the fitness function for the global search (genetic algorithm), while Pearson's correlation coefficient was used for the local search. Experimental results on the Arabic Jordanian General Tweets (AJGT) dataset demonstrated that the MA consistently improved the performance of all classifiers. The baseline accuracies were SVM (79.11%), MultinomialNB (83.89%), and LR (82.67%). After applying the MA, the accuracies improved to SVM (82.22%), MultinomialNB (85.61%), and LR (84.56%). Similar enhancements were observed in precision, recall, and F1-score across all models. The study concludes that MultinomialNB, when coupled with the MA, is the most effective classifier for this task, and the hybrid MA approach significantly boosts sentiment classification performance for the Jordanian dialect.