On using Twitter to monitor political sentiment and predict election results
Bermingham, Adam and Smeaton, Alan F. (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.
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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.
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