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Leveraging machine learning techniques to analyze consumer mindset metrics embedded in arabic dialect texts across social media platforms

artificial-intelligence
computer-education
data-mining-and-machine-learning
natural-language-and-speech
text-mining

Abstract

As social media takes a bigger role globally, analysis of Arabic discourses in a social media context can provide essential insights into consumers’ attitudes and behavior. Examining this discourse, however, presents some analytical difficulties because of the richness and variety of Arabic and its dialects. This study raises these challenges by using and comparing the performance of Machine Learning (ML) for classifying Arabic social media comments into satisfaction, loyalty, purchase intention, and service quality. The research uses machine learning models, including Support Vector Machines (SVM), Naïve Bayes, Linear Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN). Applied measures demonstrate that linear SVC outperforms all other models. Research shows that the studied models operate effectively within Arabic short text classification while confirming the usefulness of machine learning tools that extract valuable social media information from Jordanian dialect comments.
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