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Semantic Pattern Detection in COVID-19 Using Contextual Clustering and Intelligent Topic Modeling

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  • Pooja Kherwa

    (Maharaja Surajmal Institute of Technology, Delhi, India)

  • Poonam Bansal

    (Maharaja Surajmal Institute of Technology, Delhi, India)

Abstract

The Covid-19 pandemic is the deadliest outbreak in our living memory. So, it is need of hour, to prepare the world with strategies to prevent and control the impact of the epidemics. In this paper, a novel semantic pattern detection approach in the Covid-19 literature using contextual clustering and intelligent topic modeling is presented. For contextual clustering, three level weights at term level, document level, and corpus level are used with latent semantic analysis. For intelligent topic modeling, semantic collocations using pointwise mutual information(PMI) and log frequency biased mutual dependency(LBMD) are selected and latent dirichlet allocation is applied. Contextual clustering with latent semantic analysis presents semantic spaces with high correlation in terms at corpus level. Through intelligent topic modeling, topics are improved in the form of lower perplexity and highly coherent. This research helps in finding the knowledge gap in the area of Covid-19 research and offered direction for future research.

Suggested Citation

  • Pooja Kherwa & Poonam Bansal, 2021. "Semantic Pattern Detection in COVID-19 Using Contextual Clustering and Intelligent Topic Modeling," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(2), pages 1-17, July.
    • Handle: RePEc:igg:jehmc0:v:13:y:2021:i:2:p:1-17
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    References listed on IDEAS

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    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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