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Forecasting the 2016 US Presidential Elections Using Sentiment Analysis

Abstract : The aim of this paper is to make a zealous effort towards true prediction of the 2016 US Presidential Elections. We propose a novel technique to predict the outcome of US presidential elections using sentiment analysis. For this data was collected from a famous social networking website (SNW) Twitter in form of tweets within a period starting from September 1, 2016 to October 31, 2016. To accomplish this mammoth task of prediction, we build a model in WEKA 3.8 using support vector machine which is a supervised machine learning algorithm. Our results showed that Donald Trump was likely to emerge winner of 2016 US Presidential Elections.
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Submitted on : Tuesday, April 17, 2018 - 11:57:13 AM
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Prabhsimran Singh, Ravinder Sawhney, Karanjeet Kahlon. Forecasting the 2016 US Presidential Elections Using Sentiment Analysis. 16th Conference on e-Business, e-Services and e-Society (I3E), Nov 2017, Delhi, India. pp.412-423, ⟨10.1007/978-3-319-68557-1_36⟩. ⟨hal-01768531⟩

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