Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election
This provides a cost-effective method for analyzing public opinion on economic issues in elections, though it is incremental as it combines existing text mining techniques.
The paper tackled the problem of expensive and limited traditional opinion polls by proposing a computational public opinion mining approach using Twitter data to explore economic issues during the 2012 U.S. presidential election, deploying it on millions of tweets.
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.