Predicting elections from social networks based on sub-event detection and sentiment analysis

Sayan Unankard, Xue Li, Mohamed Sharaf, Jiang Zhong, Xueming Li

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

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
  • Micro-blogs
  • Sentiment analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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