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Social media as a resource for sentiment analysis of Airport Service Quality (ASQ)

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  • Martin-Domingo, Luis
  • Martín, Juan Carlos
  • Mandsberg, Glen

Abstract

User generated content (UGC) is providing new broad information datasets about airport service quality (ASQ) that are more easily available to researchers than information gathered using traditional techniques, such as surveys conducted with passengers. Research in the field is characterized by UGC provided on specialized blogs and websites. This study utilizes London Heathrow airport's Twitter account dataset and applies the sentiment analysis (SA) technique to measure ASQ. The aim of this research is to explore how SA techniques can identify new insights beyond those provided by more traditional methods. The dataset includes 4392 tweets and the SA identifies 23 attributes that can be used for comparison with other ASQ scales. Findings indicate that the frequency of passenger references to the attributes of the scale differs significantly in some cases and that the discernment of these differences can provide actionable insights for airport management when improving airport service quality.

Suggested Citation

  • Martin-Domingo, Luis & Martín, Juan Carlos & Mandsberg, Glen, 2019. "Social media as a resource for sentiment analysis of Airport Service Quality (ASQ)," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 106-115.
    • Handle: RePEc:eee:jaitra:v:78:y:2019:i:c:p:106-115
    DOI: 10.1016/j.jairtraman.2019.01.004
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    References listed on IDEAS

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    Cited by:

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    2. Barakat, H. & Yeniterzi, R. & Martín-Domingo, L., 2021. "Applying deep learning models to twitter data to detect airport service quality," Journal of Air Transport Management, Elsevier, vol. 91(C).
    3. Huseyin Arasli & Mehmet Bahri Saydam & Hasan Kilic, 2020. "Cruise Travelers’ Service Perceptions: A Critical Content Analysis," Sustainability, MDPI, vol. 12(17), pages 1-13, August.
    4. Lin, Yi-Hsin & Wu, Hanjun & He, Hang & Tsui, Kan Wai Hong, 2023. "Changes in perceptions of airport services’ attributes: An assessment of the impacts of COVID-19," Transport Policy, Elsevier, vol. 141(C), pages 116-128.
    5. Alessandro V. M. Oliveira & Bruno F. Oliveira & Moises D. Vassallo, 2024. "Airport service quality perception and flight delays: examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys," Papers 2401.02139, arXiv.org.
    6. Bellizzi, Maria Grazia & Eboli, Laura & Mazzulla, Gabriella & Postorino, Maria Nadia, 2022. "Classification trees for analysing highly educated people satisfaction with airlines’ services," Transport Policy, Elsevier, vol. 116(C), pages 199-211.
    7. Seungju Nam & Hyun Cheol Lee, 2019. "A Text Analytics-Based Importance Performance Analysis and Its Application to Airline Service," Sustainability, MDPI, vol. 11(21), pages 1-24, November.
    8. Mahmut Bakır & Şahap Akan & Emircan Özdemir & Phi-Hung Nguyen & Jung-Fa Tsai & Hong-Anh Pham, 2022. "How to Achieve Passenger Satisfaction in the Airport? Findings from Regression Analysis and Necessary Condition Analysis Approaches through Online Airport Reviews," Sustainability, MDPI, vol. 14(4), pages 1-20, February.
    9. Thampan, Aromal & Sinha, Kapil & Gurjar, B.R. & Rajasekar, E., 2020. "Functional efficiency in airport terminals: A review on Overall and Stratified Service Quality," Journal of Air Transport Management, Elsevier, vol. 87(C).
    10. Li, Lingyao & Mao, Yujie & Wang, Yu & Ma, Zihui, 2022. "How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis," Journal of Air Transport Management, Elsevier, vol. 105(C).
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