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.
Keywords
- Social media data mining
- Topic modelling
- Sentiment analysis
- Term extraction
- Airport services
- Collaborative networks
- Content analysis
- User-generated content
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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.
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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
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DOI: https://doi.org/10.1007/978-3-031-14844-6_8
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