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Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers

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  • Younggue Bae
  • Hongchul Lee

Abstract

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  • Younggue Bae & Hongchul Lee, 2012. "Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2521-2535, December.
    • Handle: RePEc:bla:jinfst:v:63:y:2012:i:12:p:2521-2535
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    File URL: http://hdl.handle.net/10.1002/asi.22768
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    Citations

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    Cited by:

    1. Wei-Lun Chang, 2019. "The Impact of Emotion: A Blended Model to Estimate Influence on Social Media," Information Systems Frontiers, Springer, vol. 21(5), pages 1137-1151, October.
    2. Joaquin Sanchez & Carmen Abril & Michael Haenlein, 2020. "Competitive spillover elasticities of electronic word of mouth: an application to the soft drink industry," Journal of the Academy of Marketing Science, Springer, vol. 48(2), pages 270-287, March.
    3. Rowe, Francisco & Mahony, Michael & Graells-Garrido, Eduardo & Rango, Marzia & Sievers, Niklas, 2021. "Using Twitter to Track Immigration Sentiment During Early Stages of the COVID-19 Pandemic," SocArXiv pc3za, Center for Open Science.
    4. Martin Quinn & Theodore Lynn & Stephen Jollands & Binesh Nair, 2016. "Domestic Water Charges in Ireland - Issues and Challenges Conveyed through Social Media," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3577-3591, August.
    5. Topaloglu, Omer & Dass, Mayukh & Kumar, Piyush, 2017. "Does who we are affect what we say and when? Investigating the impact of activity and connectivity on microbloggers' response to new products," Journal of Business Research, Elsevier, vol. 77(C), pages 23-29.

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