Effectual Text Classification in Data Mining: A Practical Approach

Abstract

Text classification is the process of setting records into classes that have already been set up based on what they say. It automatically puts texts in natural languages into categories that have already been set up. Text classification is the most crucial part of text retrieval systems, which find texts based on what the user requests, and text understanding systems, which change the text in some way, like by making summaries, answering questions, or pulling out data. Existing algorithms that use supervised learning to classify text automatically need enough examples to learn well. The algorithms for data mining are used to classify texts, as well as a review of the work that has been done on classifying texts. Design/Methodology/Approach: Data mining algorithms that are used to classify texts were talked about, and studies that looked at how these algorithms were used to classify texts were looked at, with a focus on comparative studies. Findings: No classifier can always do the best job because different datasets and situations lead to different classification accuracy. Implications for Real Life: When using data mining algorithms to classify text documents, it's important to keep in mind that the conditions of the data will affect how well the documents are classified. For this reason, the data should be well organized.