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Using sentiment analysis to reinforce learning: The case of airport community engagement

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  • Diana, Tony

Abstract

•Sentiment analysis can serve to reinforce learning by providing valuable information on the engagement context.•A contextual multi-armed bandit problem applied to airport community engagement in the case of a representative U.S. airport.•A stacking ensemble model served to predict agents' choice among three policies (exploit, explore, and pause).

Suggested Citation

  • Diana, Tony, 2022. "Using sentiment analysis to reinforce learning: The case of airport community engagement," Journal of Air Transport Management, Elsevier, vol. 102(C).
    • Handle: RePEc:eee:jaitra:v:102:y:2022:i:c:s0969699722000497
    DOI: 10.1016/j.jairtraman.2022.102228
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    References listed on IDEAS

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    1. R. H. Coase, 2013. "The Problem of Social Cost," Journal of Law and Economics, University of Chicago Press, vol. 56(4), pages 837-877.
    2. Baumol,William J. & Oates,Wallace E., 1988. "The Theory of Environmental Policy," Cambridge Books, Cambridge University Press, number 9780521322249, September.
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    Cited by:

    1. Liu, Yang & Shi, Jiale & Huang, Fei & Hou, Jingrui & Zhang, Chengzhi, 2024. "Unveiling consumer preferences in automotive reviews through aspect-based opinion generation," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    2. Wandelt, Sebastian & Sun, Xiaoqian & Zhang, Anming, 2023. "AI-driven assistants for education and research? A case study on ChatGPT for air transport management," Journal of Air Transport Management, Elsevier, vol. 113(C).

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