Comparison of SVM and Naïve Bayes Text Classification Algorithms using WEKA

Comparison of SVM and Naïve Bayes Text Classification Algorithms using WEKA
Authors : Nitin Rajvanshi, K. R. Chowdhary,
Publication Date: 14-09-2017


Author(s):  Nitin Rajvanshi, K. R. Chowdhary,

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 6 - Issue. 09 , September - 2017

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV6IS090084


Due to the growing amount of textual data, automatic methods for managing the data are needed. Automated text classification has been considered as a vital method to manage and process a large amount of documents in digital formats that are continuously increasing at an exponential rate. In general, text classification plays an important role in information extraction, summarization and text retrieval. This paper illustrates the text classification process using SVM and Naïve Bayes techniques. It automatically assigns documents to a set of classes based on the textual content of the document. In this paper after feature selection of text, machine learning algorithms Naïve Bayesian, Support Vector Machine(SVM) are applied. Evaluation and Comparison of algorithms is depicted. Topic-based text categorization classifies documents according to their topics. Performed through WEKA tool


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