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

TL;DR

This paper presents a computational approach combining sentiment analysis and topic modeling to analyze economic concerns expressed on Twitter during the 2012 US presidential election, offering a scalable alternative to traditional opinion polls.

Contribution

It introduces a novel combined text mining method for social media analysis of economic issues in elections, demonstrating its effectiveness on large-scale Twitter data.

Findings

Effectively analyzed millions of tweets about economic issues

Identified key economic concerns during the 2012 US election

Showed social media as a viable tool for public opinion mining

Abstract

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.