COVID-19 Impact Sentiment Analysis on a Topic-based Level
DOI:
https://doi.org/10.13052/jicts2245-800X.1027Keywords:
COVID-19, sentiment analysis, topic modeling, Arabic, CaMeL, Hespress, feedback, quarantine, selenium, LDAAbstract
Last December 2019, health officials in Wuhan, a province from China, identified a novel coronavirus called SARS-CoV-2 causing pneumonia. In March 2020, World Health Organization (WHO) declared COVID-19 disease being a pandemic. During quarantine periods, people all over the globe were living under severe and overwhelming circumstances and expressing feelings of loneliness, dread, and anxiety. The pandemic has had a significant impact on the labor markets. As a result, several employees have lost their jobs while others are in grave danger to lose their positions the next day. In this paper, we developed a hybrid approach integrating sentiment analysis combined with topic modeling to analyze the impact of the COVID-19 pandemic on Moroccan citizens. The data used in this study includes comments collected from a well-known news website in Morocco called Hespress. Our approach follows a two-step process. In the first step, we implement a topic modeling method to analyze and extract topics from Arabic comments, and in the second step, we perform topic-based sentiment analysis to classify people’s feedback on extracted topics. The final results revealed that the expressed sentiments regarding all the topics are highly negative.
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