%0 Conference Proceedings
%T Forecasting the 2016 US Presidential Elections Using Sentiment Analysis
%+ Guru Nanak Dev University, Punjab
%A Singh, Prabhsimran
%A Sawhney, Ravinder, Singh
%A Kahlon, Karanjeet, Singh
%Z Part 4: Social Media and Web 3.0 for Smartness
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th Conference on e-Business, e-Services and e-Society (I3E)
%C Delhi, India
%Y Arpan Kumar Kar
%Y P. Vigneswara Ilavarasan
%Y M. P. Gupta
%Y Yogesh K. Dwivedi
%Y Matti Mäntymäki
%Y Marijn Janssen
%Y Antonis Simintiras
%Y Salah Al-Sharhan
%I Springer International Publishing
%3 Digital Nations – Smart Cities, Innovation, and Sustainability
%V LNCS-10595
%P 412-423
%8 2017-11-21
%D 2017
%R 10.1007/978-3-319-68557-1_36
%K Forecasting
%K Twitter
%K Sentiment analysis
%K Support vector machine
%K WEKA
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X 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.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-01768531/document
%2 https://inria.hal.science/hal-01768531/file/978-3-319-68557-1_36_Chapter.pdf
%L hal-01768531
%U https://inria.hal.science/hal-01768531
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-I3E
%~ IFIP-LNCS-10595