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Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model.
Kumar, Vaibhav.
  • Kumar V; Data Science and Engineering, Indian Institute of Science Education and Research, Bhopal, 462066, India. vaibhav@iiserb.ac.in.
Sci Rep ; 12(1): 1849, 2022 02 03.
Article in English | MEDLINE | ID: covidwho-1671632
ABSTRACT
India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Communicable Disease Control / Decision Support Techniques / Social Media / Spatio-Temporal Analysis / Deep Learning / COVID-19 Type of study: Observational study / Prognostic study Topics: Vaccines Limits: Female / Humans / Male Country/Region as subject: Asia Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-05974-6

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Communicable Disease Control / Decision Support Techniques / Social Media / Spatio-Temporal Analysis / Deep Learning / COVID-19 Type of study: Observational study / Prognostic study Topics: Vaccines Limits: Female / Humans / Male Country/Region as subject: Asia Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-05974-6