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

Author

Listed:
  • 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)

  • Xiaoyun He

    (Department of Information Systems, Auburn University at Montgomery, Montgomery, USA)

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.

Suggested Citation

  • Amir Karami & London S. Bennett & Xiaoyun He, 2018. "Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 9(1), pages 18-28, January.
    • Handle: RePEc:igg:jsds00:v:9:y:2018:i:1:p:18-28
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    Cited by:

    1. Rondan-Cataluña, F. Javier & Peral-Peral, Begoña & Ramírez-Correa, Patricio E., 2023. "Measuring public opinion of education apps," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    2. Amir Karami & Morgan Lundy & Frank Webb & Gabrielle Turner-McGrievy & Brooke W. McKeever & Robert McKeever, 2021. "Identifying and Analyzing Health-Related Themes in Disinformation Shared by Conservative and Liberal Russian Trolls on Twitter," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    3. Amir Karami & Melek Yildiz Spinel & C. Nicole White & Kayla Ford & Suzanne Swan, 2021. "A Systematic Literature Review of Sexual Harassment Studies with Text Mining," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    4. Erdenebileg Batbaatar & Keun Ho Ryu, 2019. "Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach," IJERPH, MDPI, vol. 16(19), pages 1-19, September.
    5. Emiliano del Gobbo & Sara Fontanella & Annalina Sarra & Lara Fontanella, 2021. "Emerging Topics in Brexit Debate on Twitter Around the Deadlines," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 669-688, August.

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