Syed Shoeb Ahmed -, Mohammed Sohaib Zahoor -, Syed Shadab -, M. Shilpa -
{"title":"Leveraging Natural Language Processing Algorithms to Understand the Impact of the COVID","authors":"Syed Shoeb Ahmed -, Mohammed Sohaib Zahoor -, Syed Shadab -, M. Shilpa -","doi":"10.37082/ijirmps.v11.i1.230315","DOIUrl":null,"url":null,"abstract":"Understanding the effects of a pandemic on the public sentiment is an important challenge in the study of social dynamics during a global pandemic. This paper puts forward a case study that throws light on the psychological impact of the COVID-19 pandemic on the people living in the Indian subcontinent. The study is based on a pipeline that involves pre-processing, sentiment analysis, topic modelling, natural language processing and statistical analysis of Twitter data extracted in the form of tweets. The results demonstrate the effectiveness of this pipeline in understanding the temporal impact of the different lockdowns implemented in the span of the pandemic on the public sentiment, which can be useful for healthcare workers, authorities, and researchers.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"116 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37082/ijirmps.v11.i1.230315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding the effects of a pandemic on the public sentiment is an important challenge in the study of social dynamics during a global pandemic. This paper puts forward a case study that throws light on the psychological impact of the COVID-19 pandemic on the people living in the Indian subcontinent. The study is based on a pipeline that involves pre-processing, sentiment analysis, topic modelling, natural language processing and statistical analysis of Twitter data extracted in the form of tweets. The results demonstrate the effectiveness of this pipeline in understanding the temporal impact of the different lockdowns implemented in the span of the pandemic on the public sentiment, which can be useful for healthcare workers, authorities, and researchers.
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