On Using Twitter to Monitor Political Sentiment and Predict Election Results

This work uses the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media, and finds that social analytics using both volume-based measures and sentiment analysis are predictive.

Abstract

The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an
\nelectorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General
\nElection as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using
\nsupervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using
\nboth volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during
\nan election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content.