Advancements in Text Classification, A Comprehensive Review

This paper aims to provide a comprehensive analysis of various text classification methods that are widely used to solve text-based classification, while highlighting their strengths and understanding the limitations that can hinder the performance of classifiers. By doing so, this paper intends to enhance the awareness of text extraction possibilities in the field of machine learning (ML) and artificial intelligence (AI). Text classification is an essential aspect of data understanding, as it enables the automated categorization and organization of unstructured text data. There are different approaches used for text classification problems, such as machine learning algorithms, rule-based logics, and deep learning architectures. Each of them offers different advantages and disadvantages depending on the specific application and dataset. By analyzing these techniques in depth, we aim to provide a better understanding of their respective capabilities and limitations, allowing researchers and practitioners to select the most appropriate technique according to their problem statements. This can ultimately lead to more effective and efficient knowledge understanding from textual data in various domains, including healthcare. Overall, this review paper makes an important contribution to the field of ML and AI by providing insights into cutting-edge text categorization approaches and their practical applications.