Estimating the Effective Topics of Articles and Journals Abstract Using LDA and K-Means Clustering Algorithm

Shadikur Rahman,Umme Ayman Koana,Aras Masood Ismael ,Karmand Hussein Abdalla

doi:10.1007/978-3-030-71704-9_4

Estimating the Effective Topics of Articles and Journals Abstract Using LDA and K-Means Clustering Algorithm

Shadikur Rahman, Umme Ayman Koana + Show 2 more

https://doi.org/10.1007/978-3-030-71704-9_4

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Publication Date: Jan 1, 2021

Affiliation: Daffodil International University, Khulna University of Engineering and Technology, Sulaimani Polytechnic University, University of Raparin

#Text Clustering #Topic Modeling Algorithms + Show 8 more

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Abstract

Analyzing journals and articles abstract text or documents using topic modeling and text clustering becomes modern solutions for the increasing number of text documents. Topic modeling and text clustering are both intensely involved tasks that can benefit one another. Text clustering and topic modeling algorithms are used to maintain massive amounts of text documents. In this study, we have used LDA, K-means cluster, and also lexical database WordNet for keyphrases extraction in our text documents. K-means cluster and LDA algorithms achieve the most reliable performance for keyphrases extraction in our text documents. This study will help the researcher to make searching string based on journals and articles by avoiding misunderstandings.

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