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Predicting Elections from Social Networks Based on Sub-event Detection and Sentiment Analysis

  • Sayan Unankard
  • Xue Li
  • Mohamed Sharaf
  • Jiang Zhong
  • Xueming Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8787)

Abstract

Social networks are widely used by all kinds of people to express their opinions. Predicting election outcomes is now becoming a compelling research issue. People express themselves spontaneously with respect to the social events in their social networks. Real time prediction on ongoing election events can provide feedback and trend analysis for politicians and news analysts to make informed decisions. This paper proposes an approach to predicting election results by incorporating sub-event detection and sentiment analysis in social networks to analyse as well as visualise political preferences revealed by those social network users. Extensive experiments are conducted to evaluate the performance of our approach based on a real-world Twitter dataset. Our experiments show that the proposed approach can effectively predict the election results over the given baselines.

Keywords

election prediction event detection sentiment analysis micro-blogs 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sayan Unankard
    • 1
  • Xue Li
    • 1
  • Mohamed Sharaf
    • 1
  • Jiang Zhong
    • 2
  • Xueming Li
    • 2
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
  2. 2.Key Laboratory of Dependable Service Computing in Cyber Physical SocietyMinistry of EducationChongqingChina

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