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

TL;DR

This paper combines LDA, K-Means clustering, and WordNet to improve keyphrase extraction from article abstracts, aiding researchers in better search and categorization of scientific texts.

Contribution

It introduces a hybrid approach using LDA, K-Means, and WordNet for effective keyphrase extraction from scientific abstracts, enhancing text analysis methods.

Findings

K-Means and LDA outperform other methods in keyphrase extraction

The hybrid approach improves accuracy of topic modeling

Method facilitates better search string formulation for researchers

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.