Abstract
COVID-19 is the subject of intense and widespread discussion on social media that de facto became one of the main means for people to get and share news about the pandemic. Social media discussions may influence public opinions and could also disseminate panic and misinformation during crisis events like COVID-19 outbreak. In this context, it is crucial to detect the topics being discussed on social media and understand people perceptions, opinions and feelings by analyzing the sentiments of users towards those topics. Accordingly, this paper proposes a topic-aware sentiment analysis model. The main element of novelty of the model is that it computes the sentiment at topic level by applying a multi-label classification approach on top of the online clustering detection of the topics. The approach has been validated over a real dataset of tweets about COVID-19 in US. Results highlight that the proposed method correctly identifies the sentiment of the relevant topics like the preventive measures adopted or the curative means used. The evaluation demonstrated that the proposed sentiment classification algorithm showed higher performance compared to traditional methodologies.
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Acknowledgements
This work has been partially supported by project SERICS (PE00000014) under the NRRP MUR program funded by the EU - NGEU.
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Comito, C. (2023). How Do We Talk and Feel About COVID-19? Sentiment Analysis of Twitter Topics. In: Zhang, S., Hu, B., Zhang, LJ. (eds) Big Data – BigData 2023. BigData 2023. Lecture Notes in Computer Science, vol 14203. Springer, Cham. https://doi.org/10.1007/978-3-031-44725-9_7
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DOI: https://doi.org/10.1007/978-3-031-44725-9_7
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