Skip to main content

Exploiting User-Generated Content for Service Improvement: Case Airport Twitter Data

  • Conference paper
  • First Online:
Collaborative Networks in Digitalization and Society 5.0 (PRO-VE 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 662))

Included in the following conference series:

  • 1156 Accesses

Abstract

The study illustrates how airport collaborative networks can profit from the richness of data, now available due to digitalization. Using a co-creation process, where the passenger generated content is leveraged to identify possible service improvement areas. A Twitter dataset of 949497 tweets is analyzed from the four years period 2018–2021 – with the second half falling under COVID period - for 100 airports. The Latent Dirichlet Allocation (LDA) method was used for topic discovery and the lexicon-based method for sentiment analysis of the tweets. The COVID-19 related tweets reported a lower sentiment by passengers, which can be an indication of lower service level perceived. The research successfully created and tested a methodology for leveraging user-generated content for identifying possible service improvement areas in an ecosystem of services. One of the outputs of the methodology is a list of COVID-19 terms in the airport context.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks: A new scientific discipline. J. Intell. Manuf. 16(4), 439–452 (2005). https://doi.org/10.1007/s10845-005-1656-3

    Article  Google Scholar 

  2. Spring, M., Selviaridis, K., Zografos, K.: Coordination in service supply networks: Insights from ‘Airport Collaborative Decision Making’ (2016)

    Google Scholar 

  3. Hallikainen, H., Aunimo, L.: Adoption of digital collaborative networking platforms in companies: A study of twitter usage in Finland. In: Camarinha-Matos, L.M., Afsarmanesh, H., Ortiz, A. (eds.) PRO-VE 2020. IAICT, vol. 598, pp. 98–110. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62412-5_8

    Chapter  Google Scholar 

  4. Martin-Domingo, L., Martín, J.C., Mandsberg, G.: Social media as a resource for sentiment analysis of Airport Service Quality (ASQ). J. Air Transp. Manag. 78, 106–115 (2019). https://doi.org/10.1016/j.jairtraman.2019.01.004

    Article  Google Scholar 

  5. Bezerra, G.C.L., Gomes, C.F.: Measuring airport service quality: A multidimensional approach. J. Air Transp. Manag. 53, 85–93 (2016). https://doi.org/10.1016/j.jairtraman.2016.02.001

    Article  Google Scholar 

  6. Prentice, C., Kadan, M.: The role of airport service quality in airport and destination choice. J. Retail. Consum. Serv. 47, 40–48 (2019). https://doi.org/10.1016/j.jretconser.2018.10.006

    Article  Google Scholar 

  7. Lu, L., Mitra, A., Wang, Y.-Y., Wang, Y., Xu, P.: Use of electronic word of mouth as quality metrics: A comparison of airline reviews on twitter and skytrax (2022). https://doi.org/10.24251/HICSS.2022.165

  8. Statista: Leading countries based on number of Twitter users as of January (2022). https://www.statista.com/statistics/242606/number-of-active-twitter-users-in-selected-countries/

  9. Viri, R., Aunimo, L., Aramo-Immonen, H.: Connected and multimodal passenger transport through big data analytics: Case Tampere City Region, Finland. In: Camarinha-Matos, L.M., Afsarmanesh, H., Antonelli, D. (eds.) PRO-VE 2019. IAICT, vol. 568, pp. 527–538. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28464-0_46

    Chapter  Google Scholar 

  10. Graça, P., Camarinha-Matos, L.M.: Evaluating and influencing the performance of a collaborative business ecosystem – A simulation study. In: Camarinha-Matos, L.M., Afsarmanesh, H., Ortiz, A. (eds.) PRO-VE 2020. IAICT, vol. 598, pp. 3–18. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62412-5_1

    Chapter  Google Scholar 

  11. Müller, C., Gosling, G.D.: A framework for evaluating level of service for airport terminals. Transp. Plan. Technol. 16(1), 45–61 (1991). https://doi.org/10.1080/03081069108717470

    Article  Google Scholar 

  12. Barakat, H., Yeniterzi, R., Martín-Domingo, L.: Applying deep learning models to twitter data to detect airport service quality. J. Air Transp. Manag. 91, 102003 (2021). https://doi.org/10.1016/j.jairtraman.2020.102003

    Article  Google Scholar 

  13. Greaves, F., Ramirez-Cano, D., Millett, C., Darzi, A., Donaldson, L.: Harnessing the cloud of patient experience: Using social media to detect poor quality healthcare. BMJ Qual. Saf. 22(3), 251–255 (2013). https://doi.org/10.1136/bmjqs-2012-001527

    Article  Google Scholar 

  14. Kumar, S., Kar, A.K., Ilavarasan, P.V.: Applications of text mining in services management: A systematic literature review. Int. J. Inf. Manage. Data Insights 1(1), 100008 (2021). https://doi.org/10.1016/j.jjimei.2021.100008

    Article  Google Scholar 

  15. Bae, W., Chi, J.: Content analysis of passengers’ perceptions of airport service quality: The case of Honolulu International Airport. J. Risk Fin. Manage. 15(1), 5 (2021). https://doi.org/10.3390/jrfm15010005

    Article  Google Scholar 

  16. Gitto, S., Mancuso, P.: Improving airport services using sentiment analysis of the websites. Tourism Manage. Perspect. 22, 132–136 (2017). https://doi.org/10.1016/j.tmp.2017.03.008

    Article  Google Scholar 

  17. Lee, K., Yu, C.: Assessment of airport service quality: A complementary approach to measure perceived service quality based on Google reviews. J. Air Transp. Manag. 71, 28–44 (2018). https://doi.org/10.1016/j.jairtraman.2018.05.004

    Article  Google Scholar 

  18. Martín-Domingo, L., Martín, J.C.: The effect of COVID-related EU state aid on the level playing field for airlines. Sustainability 14(4), 2368 (2022). https://doi.org/10.3390/su14042368

    Article  Google Scholar 

  19. Ma, H., et al.: COVID term: A bilingual terminology for COVID-19. BMC Med. Inform. Decis. Mak. 21(1), 231 (2021). https://doi.org/10.1186/s12911-021-01593-9

    Article  Google Scholar 

  20. Merriam-Webster: We Added 455 New Words to the Dictionary for October 2021 (2022). https://www.merriam-webster.com/words-at-play/new-words-in-the-dictionary. Accessed 04 May 2022

  21. Christodoulou, E., Gregoriades, A., Pampaka, M., Herodotou, H.: Combination of topic modelling and decision tree classification for tourist destination marketing. In: Dupuy-Chessa, S., Proper, H.A. (eds.) CAiSE 2020. LNBIP, vol. 382, pp. 95–108. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49165-9_9

    Chapter  Google Scholar 

  22. Kaveski Peres, C., Pacheco Paladini, E.: Exploring the attributes of hotel service quality in Florianópolis-SC, Brazil: An analysis of tripAdvisor reviews. Cogent Business & Management 8(1), 1926211 (2021). https://doi.org/10.1080/23311975.2021.1926211

    Article  Google Scholar 

  23. Kiliç, S., Çadirci, T.O.: An evaluation of airport service experience: An identification of service improvement opportunities based on topic modeling and sentiment analysis. Res. Transp. Bus. Manage. 43, 100744 (2021). https://doi.org/10.1016/j.rtbm.2021.100744

    Article  Google Scholar 

  24. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  25. Mattmann, C.A., Zitting, J.L.: Tika in action. Manning (2012)

    Google Scholar 

  26. WHO: Statement on the Second Meeting of the International Health Regulations (2005) Emergency Committee Regarding the Outbreak of Novel Coronavirus (2019-nCoV) (2020). https://web.archive.org/web/20210815071616/https://www.who.int/news/item/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-%282005%29-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-%282019-ncov%29

  27. Berthold, M.R., et al.: KNIME - the Konstanz information miner. ACM SIGKDD Explorations Newsl. 11(1), 26–31 (2009). https://doi.org/10.1145/1656274.1656280

    Article  Google Scholar 

  28. Maas, A., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 142–150 (2011)

    Google Scholar 

  29. Newman, D., Asuncion, A., Smyth, P., Welling, M.: Distributed algorithms for topic models. J. Mach. Learn. Res. 10, 1801–1828 (2009)

    MathSciNet  MATH  Google Scholar 

  30. Yao, L., Mimno, D., McCallum, A.: Efficient methods for topic model inference on streaming document collections. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 937–946 (2009). https://doi.org/10.1145/1557019.1557121

  31. McCallum, A.K.: Mallet: A machine learning for language toolkit (2002). http://mallet.cs.umass.edu. Accessed 19 Jun 2022

  32. Chinnov, A., Kerschke, P., Meske, C., Stieglitz, S., Trautmann, H.: An Overview of Topic Discovery in Twitter Communication through Social Media Analytics (2005)

    Google Scholar 

  33. Punel, A., Ermagun, A.: Using Twitter network to detect market segments in the airline industry. J. Air Transp. Manag. 73, 67–76 (2018). https://doi.org/10.1016/j.jairtraman.2018.08.004

    Article  Google Scholar 

Download references

Acknowledgments

This research was partly funded by the Ministry of Education and Culture in Finland through the Future Expertise in Sales and Services (FESS) Research Project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lili Aunimo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aunimo, L., Martin-Domingo, L. (2022). Exploiting User-Generated Content for Service Improvement: Case Airport Twitter Data. In: Camarinha-Matos, L.M., Ortiz, A., Boucher, X., Osório, A.L. (eds) Collaborative Networks in Digitalization and Society 5.0. PRO-VE 2022. IFIP Advances in Information and Communication Technology, vol 662. Springer, Cham. https://doi.org/10.1007/978-3-031-14844-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-14844-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14843-9

  • Online ISBN: 978-3-031-14844-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics