An Efficient Topic Modeling Approach for Text Mining and Information Retrieval through K-means Clustering

JUNAID RASHID; SYED MUHAMMAD ADNAN SHAH; AUN IRTAZA

doi:10.22581/muet1982.2001.20

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An Efficient Topic Modeling Approach for Text Mining and Information Retrieval through K-means Clustering

Author(s)

JUNAID RASHID Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

SYED MUHAMMAD ADNAN SHAH Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

AUN IRTAZA Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

Abstract

Topic modeling is an effective text mining and information retrieval approach to organizing knowledge with various contents under a specific topic. Text documents in form of news articles are increasing very fast on the web.Analysis of these documents is very important in the fields of text mining and information retrieval. Meaningful information extraction from these documents is a challenging task. One approach for discovering the theme from text documents is topic modeling but this approach still needs a new perspective to improve its performance. In topic modeling, documents have topics and topics are the collection of words. In this paper, we propose a new k-means topic modeling (KTM) approach by using the k-means clustering algorithm. KTM discovers better semantic topics from a collection of documents. Experiments on two real-world Reuters 21578 and BBC News datasets show that KTM performance is better than state-of-the-art topic models like LDA(Latent DirichletAllocation) and LSA(Latent Semantic Analysis). The KTM is also applicable for classification and clustering tasks in text mining and achieves higher performance with a comparison of its competitors LDA and LSA.

Publication Details

Page(s) 213-222

DOI 10.22581/muet1982.2001.20

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 39, Issue: 1, Year: 2020

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Keywords

entropy, principal component analysis, K-Means, Bag-of-words, topic modeling, Local term weighting

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