Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election

Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election

Amir Karami (School of Library and Information Science, University of South Carolina, Columbia, USA), London S. Bennett (University of South Carolina Honors College, Columbia, USA) and Xiaoyun He (Department of Information Systems, Auburn University at Montgomery, Montgomery, USA)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/IJSDS.2018010102
OnDemand PDF Download:
No Current Special Offers


Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This article proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.
Article Preview

Among social media, Twitter is the most popular social media for researchers because it is very convenient to collect the activity of users. Based on this feature, a wide range of studies have been developed, from business (Mishne & Glance, 2006) to public response to public health (Karami & Shaw, 2017; Karami et al., 2018). In this section, we review the political studies using Twitter data for two purposes: election prediction and election analysis.

Complete Article List

Search this Journal:
Open Access Articles
Volume 13: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing