Please use this identifier to cite or link to this item:
http://nopr.niscpr.res.in/handle/123456789/61835| metadata.dc.identifier.doi: | https://doi.org/10.56042/alis.v70i1.71939 |
| Title: | Topic modelling-based analysis of COVID-19 vaccine articles published in the preprint server MedRxiv |
| Authors: | Deshpande, Nishad Ligade, Virendra Shaikh, Shabib-Ahmed Khode, Alok |
| Keywords: | COVID-19;Vaccine;Preprints;LDA;Topic modelling |
| Issue Date: | May-2023 |
| Publisher: | NIScPR-CSIR, India |
| Abstract: | Two thousand one hundred and ninety-eight research publications on COVID-19 vaccines in MedRxiv preprint repository during January 01, 2020 and December 31, 2021 were analyzed for topic modelling with unsupervised inference method. Latent Dirichlet Allocation (LDA) method was used to investigate the thematic structure of the preprints. It was observed that the published articles were related to either clinical trials or patient responses to vaccine or modelling for various applications such as infection transmission, vaccine allocation, vaccine hesitancy etc. |
| Page(s): | 41-51 |
| ISSN: | 0975-2404 (Online); 0972-5423 (Print) |
| Appears in Collections: | ALIS Vol.70(1) [March 2023] |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ALIS Vol.70(1) 41-51.pdf | 10.02 MB | Adobe PDF | View/Open |
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