On using Twitter to monitor political sentiment and predict election results
Bermingham, Adam and Smeaton, Alan F.ORCID: 0000-0003-1028-8389
(2011)
On using Twitter to monitor political sentiment and predict election results.
In: Sentiment Analysis where AI meets Psychology (SAAIP) Workshop at the International Joint Conference for Natural Language Processing (IJCNLP), 13th November 2011, Chiang Mai, Thailand.
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
electorate? 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
Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using
supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using
both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during
an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content.