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

• URL: http://arxiv.org/abs/2508.16046v1
• Date: Fri, 22 Aug 2025 02:51:33 GMT
• Title: Estimating the Effective Topics of Articles and journals Abstract Using LDA And K-Means Clustering Algorithm
• Authors: Shadikur Rahman, Umme Ayman Koana, Aras M. Ismael, Karmand Hussein Abdalla,
• Abstract summary: We have used LDA, K-Means cluster and also lexical database WordNet for keyphrases extraction in our text documents.<n>This study will help the researcher to make a search string based on journals and articles by avoiding misunderstandings.
• Score: 0.2765128393602164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Analyzing journals and articles abstract text or documents using topic modelling and text clustering has become a modern solution for the increasing number of text documents. Topic modelling and text clustering are both intensely involved tasks that can benefit one another. Text clustering and topic modelling 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 keyphrase extraction in our text documents. This study will help the researcher to make a search string based on journals and articles by avoiding misunderstandings.

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