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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.

Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Subjects:Computer Science > Computational linguistics
Computer Science > Information technology
Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > World Wide Web
DCU Faculties and Centres:Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:16670
Deposited On:02 Dec 2011 09:23 by Adam Bermingham. Last Modified 12 Jan 2017 12:27

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