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International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
IJARCCE adheres to the suggestive parameters outlined by the University Grants Commission (UGC) for peer-reviewed journals, upholding high standards of research quality, ethical publishing, and academic excellence.
← Back to VOLUME 13, ISSUE 5, MAY 2024

Topic Modeling With Latent Dirichlet Allocation(LDA) using Machine Learning

Karishma Borse, Pingale Divya Vijay, Mahajan Pornima Dattatraya, Patil Komal Vinod

DOI: 10.17148/IJARCCE.2024.13589

Abstract: Topic modeling is a very efficient data mining technique for mining text, latent data identification, and establishing links between text documents and data. Many studies in this field have been published by researchers, and these findings have been implemented in linguistic science, software engineering, political science, and medicine, among other fields.

Keywords: Topic Modeling, Latent Dirichlet Allocation, Machine Learning, Applications.

How to Cite:

[1] Karishma Borse, Pingale Divya Vijay, Mahajan Pornima Dattatraya, Patil Komal Vinod, “Topic Modeling With Latent Dirichlet Allocation(LDA) using Machine Learning,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2024.13589