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 years
due to its ability to extract valuable insights from large volumes of unstructured textual data. This review aims to
provide a comprehensive evaluation of the applicability of text mining techniques across various domains and
industries.
The review starts off with a dialogue of the basic ideas and methodologies that are concerned with textual content
mining together with preprocessing, feature extraction, and machine learning algorithms.
Furthermore, this survey highlights the challenges faced at some stage in implementing textual content mining
strategies. Additionally, the review explores emerging tendencies and possibilities in text-mining research. It
discusses advancements in deep learning models for text evaluation, integration with different AI technologies like
image or speech recognition for multimodal analysis, utilization of domain-unique ontologies or information graphs
for more desirable information of textual facts, and incorporation of explainable AI strategies to improve
interpretability. The findings from this overview are analyzed to identify common developments and patterns in text
mining packages across extraordinary domain names.
The consequences of this paper will advantage researchers by means of imparting updated expertise of modern
practices in textual content mining. Additionally, it will manual practitioners in selecting suitable strategies for their
unique application domain names while addressing capacity-demanding situations.