A Practical and Empirical Comparison of Three Topic Modeling Methods Using a COVID-19 Corpus: LSA, LDA, and Top2Vec

Date
2023-01-03
Authors
Zengul, Ferhat
Bulut, Aysegul
Oner, Nurettin
Ahmed, Abdulaziz
Yadav, Manju
Gray, Hope Gracie
Ozaydin, Bunyamin
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930
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Abstract
This study was prepared as a practical guide for researchers interested in using topic modeling methodologies. This study is specially designed for those with difficulty determining which methodology to use. Many topic modeling methods have been developed since the 1980s namely, latent semantic indexing or analysis (LSI/LSA), probabilistic LSI/LSA (pLSI/pLSA), naïve Bayes, the Author-Recipient-Topic (ART), Latent Dirichlet Allocation (LDA), Topic Over Time (TOT), Dynamic Topic Models (DTM), Word2Vec, Top2Vec, and \variation and combination of these techniques. Researchers from disciplines other than computer science may find it challenging to select a topic modeling methodology. We compared a recently developed topic modeling algorithm Top2Vec with two of the most conventional and frequently-used methodologiesLSA and LDA. As a study sample, we used a corpus of 65,292 COVID-19-focused abstracts. Among the 11 topics we identified in each methodology, we found high levels of correlation between LDA and Top2Vec results, followed by LSA and LDA and Top2Vec and LSA. We also provided information on computational resources we used to perform the analyses and provided practical guidelines and recommendations for researchers.
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Data, Text, and Web Mining for Business Analytics, covid-19, lda, lsa, top2vec, topic modeling
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10
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Proceedings of the 56th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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