Abstract
Microblogging has evolved as a popular and powerful platform among the internetworked users. Millions of people share ideas on several aspects of daily life. This makes the microblogging portals an enormous source of data that can be used for sentiment analysis. In particular, the social media is generating a vast amount of public responses in the form of tweets, replies, comments, etc. The emotional analysis on Twitter provides means to survey public emotions related to the products, policies, and politics happening around. The aim of our initiative is to develop an approach to identify the positive and negative Twitter responses in context to the public trend. This paper portrays sentiment analysis by extracting tweets data being contributed to a particular social media trend. We developed a web application with an intuitive user interface using React and Django which displays the live trends, and the Django server fetches the trending topics from Twitter periodically which classifies and analyzes the same by extracting the tweets using the Twitter API. Our model has analyzed the sentiment distribution, along with the frequency and trend of public responses spatially as well as temporally.
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Agarwal, A., Shreeji, Jain, R., Sinha, A. (2023). Social Media Listening for Public Response Analysis on Twitter Network. In: Khanna, A., Gupta, D., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Third Doctoral Symposium on Computational Intelligence . Lecture Notes in Networks and Systems, vol 479. Springer, Singapore. https://doi.org/10.1007/978-981-19-3148-2_54
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DOI: https://doi.org/10.1007/978-981-19-3148-2_54
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