This review article provides a thorough assessment of modern and innovative algorithms for text classification through both observational and experimental evaluations. We propose a new classification system, grounded in methodology, to categorize text classification algorithms into an organized structure from general categories down to particular fine-grained techniques. Drawing on more than 100 academic papers from prominent publishers, our extensive review spans a wide range of algorithms, encompassing traditional, deep learning, and emerging approaches. Through observational studies and comparative experiments among various algorithms, techniques, and methodological categories, we offer detailed insights into the area of text classification. The goal of this survey is to assist scholars in choosing the right methods for specific projects while encouraging further advancements in this area. This detailed examination not only contributes to the scholarly conversation on text classification but also seeks to direct future progress by identifying promising avenues for innovation and enhancement. The primary contributions of this article include the sophisticated methodological classification, a thorough review and examination of state-of-the-art algorithms, along with observational and experimental assessments, and a visionary outlook on the future development of text classification methods.

This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems, addressing the lack of depth and precision in existing literature. It proposes a two-pronged approach: a thorough analysis of current algorithms and a novel, hierarchical taxonomy for precise categorization. The taxonomy is based on a tri-level hierarchy, starting with the methodology category and narrowing down to specific techniques. Such a framework allows for a structured and comprehensive classification of algorithms, assisting researchers in understanding the interrelationships among diverse algorithms and techniques. Covering a wide range of algorithms, this taxonomy first categorizes algorithms into four main analysis types: user and item similarity based methods, hybrid and combined approaches, deep learning and algorithmic methods, and mathematical modeling methods, with further subdivisions into sub-categories and techniques. The paper incorporates both empirical and experimental evaluations to differentiate between the techniques. The empirical evaluation ranks the techniques based on four criteria. The experimental assessments rank the algorithms that belong to the same category, sub-category, technique, and sub-technique. Also, the paper illuminates the future prospects of big data techniques in recommendation systems, underscoring potential advancements and opportunities for further research in this fields.