Abstract
Online social media like Twitter and Facebook provides a common platform for presenting views and opinions of an individual on various events. This research work aims at detecting peak events that occur in online social networks using the proposed approach of exponential moving average algorithm and peak recognition method. A two-level hybrid sentiment analysis using n-grams and Naïve Bayes classifiers is performed on tweets to ensure the true sentiment of the user. The analysis was based on bag-of-words model and Bill McDonald’s list for positive and negative words. The tweets were streamed for #BiggBoss and stored in sequential bins. The potential event was detected on the final day of result announcement by peak recognition method. The Naïve Bayes classifier predicted tweets with accuracy of 89% which would further aid in event summarization and eliminate event-related rumors.
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Geetha, R., Suthanthira Devi, P., Karthika, S. (2020). #BiggBoss—Long-Run Event Detection and Sentiment Mining in Twitter. In: Tuba, M., Akashe, S., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Advances in Intelligent Systems and Computing, vol 933. Springer, Singapore. https://doi.org/10.1007/978-981-13-7166-0_22
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DOI: https://doi.org/10.1007/978-981-13-7166-0_22
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