An Automatic Stop Words Removal in Maghrebi Arabic Dialect Text Classification Using Part of Speech Tagging

The chapter explores the intricate challenges posed by Maghrebi Arabic dialects in the realm of natural language processing, with a particular focus on text classification. It begins by examining the linguistic diversity and complexity of Maghrebi Arabic, which encompasses dialects from countries like Morocco and Algeria, and is influenced by various linguistic elements, including Arabic, Berber, French, and Spanish. The text delves into the significance of stop words removal as a crucial preprocessing step in sentiment analysis, categorizing stop words into global, subject-specific, and document-level terms, and discussing their impact on text classification tasks. The chapter presents an in-depth review of existing literature, comparing manual, automatic, and hybrid stop words removal methods, and their effectiveness across different Arabic dialects and datasets. It introduces a novel approach to automatic stop words removal using part-of-speech tagging, which aims to reduce dependency on specific languages and datasets. The methodology section outlines the use of two datasets, AfriSenti and MAC, and describes the preprocessing, feature extraction, and deep learning architectures employed in the study. The experimentation results highlight the superior performance of the proposed POS-based stop words removal technique, particularly when combined with specific feature extractors and deep learning architectures. The chapter concludes by discussing the implications of the findings and suggesting directions for future research, emphasizing the need for more adaptable and dialect-specific methods in Arabic NLP.