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Analysis of Text Classification Algorithms: A Review

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Analysis of Text Classification Algorithms: A Review


Nida Zafar Khan | Prof. S. R. Yadav

https://doi.org/10.31142/ijtsrd21448



Nida Zafar Khan | Prof. S. R. Yadav "Analysis of Text Classification Algorithms: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2, February 2019, pp.579-581, URL: https://www.ijtsrd.com/papers/ijtsrd21448.pdf

Classification of data has become an important research area. The process of classifying documents into predefined categories based on their content is Text classification. It is the automated assignment of natural language texts to predefined categories. The primary requirement of text retrieval systems is text classification, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as answering questions, producing summaries or extracting data. In this paper we are studying the various classification algorithms. Classification is the process of dividing the data to some groups that can act either dependently or independently. Our main aim is to show the comparison of the various classification algorithms like K-nn, Naïve Bayes, Decision Tree, Random Forest and Support Vector Machine (SVM) with rapid miner and find out which algorithm will be most suitable for the users.

Text Mining, K-nn, Naïve Bayes, Decision Tree, Random Forest and Support Vector Machine, Rapid miner


IJTSRD21448
Volume-3 | Issue-2, February 2019
579-581
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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