Advancements in Text Classification: A Comprehensive Survey of Machine Learning and Deep Learning Models

Abstract

This paper offers a comprehensive survey of recent advancements in machine learning (ML) and deep learning (DL) models, specifically focusing on their applications in text classification, emphasizing sentiment analysis and data retrieval. The authors initiated the discussion by presenting an overview of traditional ML techniques, such as Support Vector Machines and Naive Bayes, establishing a foundational context for exploring the transformative impact of DL methodologies. The manuscript delves into key innovations in deep learning, including attention mechanisms and various neural network architectures, such as recurrent and convolutional networks, analyzing their contributions to enhancing text classification tasks. Furthermore, the study identifies critical research gaps and challenges in the existing literature, underscoring the necessity for hybrid models that integrate the strengths of both ML and DL approaches. A significant focus is placed on interpretability and explainability in complex models, ensuring transparency in decision-making processes. Additionally, the manuscript addresses the challenges associated with unbalanced datasets and proposes robust strategies to mitigate the vulnerability of models to adversarial attacks. This survey aims to equip researchers and practitioners with valuable insights into the latest text classification techniques by providing a detailed roadmap for future research. The findings underscore the importance of developing tailored models to address domain-specific challenges and advocate for continued innovation within natural language processing. This comprehensive review is essential for advancing the understanding and application of text classification methodologies across diverse contexts.