A Review Of Text Mining Techniques: Trends, and Applications In Various Domains

Abstract

Text mining, a subfield of natural language processing (NLP), has received considerable attention in recent yearsdue to its ability to extract valuable insights from large volumes of unstructured textual data. This review aims toprovide a comprehensive evaluation of the applicability of text mining techniques across various domains andindustries.The review starts off with a dialogue of the basic ideas and methodologies that are concerned with textual contentmining together with preprocessing, feature extraction, and machine learning algorithms.Furthermore, this survey highlights the challenges faced at some stage in implementing textual content miningstrategies. Additionally, the review explores emerging tendencies and possibilities in text-mining research. Itdiscusses advancements in deep learning models for text evaluation, integration with different AI technologies likeimage or speech recognition for multimodal analysis, utilization of domain-unique ontologies or information graphsfor more desirable information of textual facts, and incorporation of explainable AI strategies to improveinterpretability. The findings from this overview are analyzed to identify common developments and patterns in textmining packages across extraordinary domain names.The consequences of this paper will advantage researchers by means of imparting updated expertise of modernpractices in textual content mining. Additionally, it will manual practitioners in selecting suitable strategies for theirunique application domain names while addressing capacity-demanding situations.