Abstract
We are noticing a new era of social networks where in a blink of eye millions of tweets about any topic can be emerged. Especially, when an event like national election comes for a nation, the messages in social media especially twitter rises at its peak. The amount of data twitter has during that time is enormous and those tweets were never been used to analyze anyone’s popularity. Our work is focused on predicting a presidential candidate’s live popularity through sentiment analysis. We design the system to predict the popularity by a single day. To do this several features from tweets of a particular day have been passed through a dimensionality reduction algorithm, e.g., PCA (Principal Component Analysis). Consequently, the PCA components have been exercised into a fuzzy system. In particular, we used ANFIS (Adaptive Neuro Fuzzy Inference System) to predict a presidential candidate’s popularity on a single day.
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Mazumder, P., Chowdhury, N.A., Anwar-Ul-Azim Bhuiya, M., Akash, S.H., Rahman, R.M. (2018). A Fuzzy Logic Approach to Predict the Popularity of a Presidential Candidate. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_6
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