  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 paper 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.
