Semantic Emotion in COVID-19 Online News
Abstract
This research aimed to determine the dominant semantic emotion in the online news about COVID-19. The study’s data included 28 sentences from five news sources by two publishers – CNN News and Jakarta Post. This study used qualitative research method and Shaver’s theory in the analysis of the data. It was found that the news sentences were rich in semantic emotions. 12 sentences (43%) contained fear, five sentences (18%) contained sadness, and four sentences (14%) contained joy. Seven sentences did not contain basic emotions because they only provided information. So, it can be concluded that the dominant emotions used in COVID-19 online news are fear emotions. By knowing the semantics emotions in the news, people can fully understand the purpose of the news.
Keywords: semantic, emotion, COVID-19, online news
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