Forecasting the 2016 US Presidential Elections Using Sentiment Analysis - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

Forecasting the 2016 US Presidential Elections Using Sentiment Analysis

(1) , (1) , (1)
1
Prabhsimran Singh
  • Function : Author
  • PersonId : 1030986
Ravinder Singh Sawhney
  • Function : Author
  • PersonId : 1030987
Karanjeet Singh Kahlon
  • Function : Author
  • PersonId : 1030988

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.
Fichier principal
Vignette du fichier
978-3-319-68557-1_36_Chapter.pdf (799.59 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01768531 , version 1 (17-04-2018)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Prabhsimran Singh, Ravinder Singh Sawhney, Karanjeet Singh 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⟩
85 View
458 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More