Abstract
Social media has witnessed a remarkable surge in popularity, serving as a platform where individuals express their views on crucial subjects, including politics, infectious diseases, social movements, and security issues. This profusion of readily accessible information empowers decision-makers and stakeholders to shape policies and strategies grounded in statistical insights into societal dynamics. To facilitate an in-depth collaborative analysis of social trends and public sentiment, we developed TweetVi - an interactive dashboard. Within the interactive dashboard, we’ve developed a pipeline encompassing three machine-learning components: language detection, spam detection, and sentiment analysis. This collaborative platform further provides analytical results on topical sentiments and trends, fostering a deeper understanding of the social landscape. The TweetVi application empowers users to gain valuable insights into evolving social narratives and sentiments, facilitating evidence-based decision-making in a dynamic online environment.
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Graham, M.L.W. et al. (2023). TweetVi: A Tweet Visualisation Dashboard for Automatic Topic Classification and Sentiment Analysis. In: Goh, D.H., Chen, SJ., Tuarob, S. (eds) Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration. ICADL 2023. Lecture Notes in Computer Science, vol 14457. Springer, Singapore. https://doi.org/10.1007/978-981-99-8085-7_14
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DOI: https://doi.org/10.1007/978-981-99-8085-7_14
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