The rapid growth of digital platforms has resulted in massive volumes of unstructured textual data generated from social media, academic repositories, healthcare systems, and business applications. Extracting meaningful information from such data is a challenging task due to the complexity and ambiguity of natural language. Text mining has emerged as a powerful approach for transforming unstructured text into structured knowledge through techniques drawn from information retrieval, natural language processing, and machine learning. This paper presents a comprehensive review of text mining methodologies, techniques, tools, and application domains. Both traditional statistical approaches and modern deep learning-based methods are discussed and compared in terms of efficiency, interpretability, and applicability. The review aims to provide a consolidated reference for researchers and practitioners working in the field of text analytics.
Kamble et al. (Sun,) studied this question.