Anna Kamille D. Balan
ABSTRACT
Twitter has become a host of individuals’ expressions of sentiments and emotion given the effect and consequences of the COVID-19 pandemic. Supported by the appraisal theory of emotion [1], this descriptive cross-sectional (Type 2) study described the pattern of sentiments and emotions over the course of COVID-19 outbreak in accordance to gender. In the following analysis, Gupta et al.'s (2020) COVID-19 twitter dataset and the additional scraped data from September 2021 – December 2021 was used in the present study. The methodologies heavily relied on topic modeling techniques and pre-trained machine learning-based emotion analytic algorithms. Results showed that the negative sentiments surrounding the media is caused by using keywords like covid19, coronavirus, pandemic, and cases but, it appeared to be in a joyful emotion as tweets are expressed in prayers and social solidarity. Furthermore, an increase in the number of reported COVID-19 cases also increases the intensity of fear in females. Implementation of lockdown, quarantines and strict measures lead to higher anger for males. Lastly, with the continuous vaccine rollouts, positivity and optimism are being more expressed across threads. Given the rise in COVID-19 cases, these findings provide sentiment analysis in a population which can be used to improve COVID-19 management through policies and projects in supporting the well-being of the society.
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Index Terms
- Analysis of Sentiments and Emotions Attributes of COVID-19-related tweets in the Philippines Using time-Series Analysis
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