A corpus-based sentiment analysis of COVID-19 vaccination news reports / Siti Nur Aina Mohd Hashim - Siti Nur Aina , Mohd Hashim (2024)
COVID-19 is the world's most critical global health emergency at present and administering an effective vaccination program is crucial in keeping the pandemic under control. However, the mainstream views on COVID-19 vaccinations are rather divided. By using a corpus-based approach, this study intends to investigate how sentiments regarding COVID-19 vaccination are reflected in linguistic elements and how such sentiments change over time in a local online newspaper in Malaysia. Adopting a mixed method approach, this study employs NVivo and Wmatrix and the selected news articles will be carried out by descriptive analysis to gain insights into elements that constitute sentiments of COVID-19 vaccines. Furthermore, the linguistic elements are examined using discursive news value analysis (DNVA) in pursuance of the transition in sentiments between 2020 and 2022. Based on NVivo and Wmatrix results, the sentiment is negative as the words pertaining to vaccinations consist of more words with negative connotations compared to positive ones. The findings identified three themes in 2020 and eight themes in each of 2021 and 2022. The transition in vaccination sentiments was portrayed as positive from 2020 to negative in 2021 and neutral in 2022 as indicated by the amounts of themes. The limitation of this study is that the researcher only focuses on a limited time frame (the month of March in 2020, 2021 and 2022) and only indicates the sentiment at that certain period. This study is significant in providing insights into the public’s attitudes, underlying concerns and acceptance of the vaccines, which can be utilized to inform and improve vaccination policies.