Geo-sentiment trends analysis of tweets in context of economy and employment during COVID-19

Narendranath Sukhavasi (), Janardan Misra (), Vikrant Kaulgud () and Sanjay Podder ()
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Narendranath Sukhavasi: Accenture
Janardan Misra: Accenture
Vikrant Kaulgud: Accenture
Sanjay Podder: Accenture

Journal of Computational Social Science, 2023, vol. 6, issue 2, No 1, 441 pages

Abstract: Abstract To effectively design policies and implement measures for addressing problems faced by people during these difficult times of pandemic, it is critical to have a clear vision of the problems people are freely talking about. One of the ways is to analyze social media feeds e.g., tweets, which has become one of the primary ways people express their views on various socioeconomic issues and on-ground effectiveness of measures adopted to address these issues. In this work, we attempt to uncover various socioeconomic issues, which are giving rise to negative and positive sentiments and their trends across geographies over a course of one year of the pandemic. We also try identifying similarities and differences in opinions as they vary across gender as the time passes through the crisis. Many previous works have analyzed sentiments in context of vaccines, fatalities, and lockdowns; however, socioeconomic issues did not receive full attention. We found that sentiments of people with respect to economy are negative across geographies during starting of pandemic. Thereafter, gradually sentiments lift towards positive direction reflecting a sense of improvement in situation. Females appeared to have slightly different concerns and hopes in comparison to males and especially across globe people expressed positive sentiments during new year time. Finally, this work, together with many other similar works on social media analysis gives ground for wide scale adoption of geo-temporal sentiments trend analysis of social media as a tool for uncovering key concerns and effectiveness of measures.

Keywords: Tweet analytics; Geo-temporal trend mining; Sentiment analysis; Gender opinion analysis (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s42001-023-00201-2

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