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

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          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 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.

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          Most cited references 16

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          Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures.

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          We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses--which is consistent with the effects of sleep and circadian rhythm--and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends.
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            Lexicon-Based Methods for Sentiment Analysis

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                Author and article information

                Journal
                International Journal of Strategic Decision Sciences
                IGI Global
                1947-8569
                1947-8577
                January 2018
                January 2018
                : 9
                : 1
                : 18-28
                Affiliations
                [1 ]School of Library and Information Science, University of South Carolina, Columbia, USA
                [2 ]University of South Carolina Honors College, Columbia, USA
                [3 ]Department of Information Systems, Auburn University at Montgomery, Montgomery, USA
                Article
                10.4018/IJSDS.2018010102
                © 2018

                Developmental biology, Ecology

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