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The Impact of Emotion: A Blended Model to Estimate Influence on Social Media

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  • Wei-Lun Chang

    (Tamkang University)

Abstract

The goal of this research is to devise a model of social influence with sentiment analysis and help organization discover real influential people on social media. This study takes into account the quality of post and sentiment ratio simultaneously. We discovered the meaning of sentiment behind post, retweet, and reply is more important than numbers. This research selected four targets (two politicians and two celebrities) on Twitter to examine the proposed model. The results revealed the sentiment ratio of celebrities is higher than politicians. The reason may be the celebrities posted random issues in daily life and followers all supported them. However, the politicians’ tweets are easy to provoke a conflict which may cause emotional expressions from fans or followers. Sentiment analysis can adjust numbers based on the insights of content. We also provided the h-index to identify high impact of posted topics. The results showed various topics have different impact according to h-index. In summary, the proposed model can appropriately estimate the influence of a person in social media and assist firms allocate marketing resources efficiently.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:infosf:v:21:y:2019:i:5:d:10.1007_s10796-018-9824-0
    DOI: 10.1007/s10796-018-9824-0
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    References listed on IDEAS

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    1. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
    2. Younggue Bae & Hongchul Lee, 2012. "Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2521-2535, December.
    3. 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.
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    Cited by:

    1. Yu Lehe & Gui Zhengxiu, 2021. "Analysis of Enterprise Social Media Intelligence Acquisition Based on Data Crawler Technology," Entrepreneurship Research Journal, De Gruyter, vol. 11(2), pages 3-23, April.
    2. Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 0. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    3. Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 2020. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 22(5), pages 1053-1066, October.
    4. Mohammadreza Mousavizadeh & Mehrdad Koohikamali & Mohammad Salehan & Dam J. Kim, 2022. "An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Elaboration Likelihood Model," Information Systems Frontiers, Springer, vol. 24(1), pages 211-231, February.
    5. Jyoti Choudrie & Shruti Patil & Ketan Kotecha & Nikhil Matta & Ilias Pappas, 2021. "Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study," Information Systems Frontiers, Springer, vol. 23(6), pages 1431-1465, December.

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