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Text mining, a crucial field within data mining, focuses on extracting meaningful patterns, trends, and knowledge from unstructured textual data. With the exponential growth of digital content, effective classification techniques have become essential for organizing and interpreting large text datasets. This paper reviews various data mining classification techniques applied in text mining, including Decision Trees, Naïve Bayes, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Neural Networks. Each method offers unique strengths in handling high-dimensional text data and improving the accuracy of document categorization, sentiment analysis, and topic detection. The study examines the comparative performance of these algorithms based on factors such as precision, recall, computational efficiency, and adaptability to large-scale datasets. Additionally, it discusses recent advancements in hybrid and ensemble models that combine multiple classifiers to enhance predictive performance. The review concludes that the selection of an appropriate classification technique largely depends on the nature of the text data, feature representation methods, and the specific application domain.
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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: K. Kantha Raju, kanakaraju8@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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