Author
Aunimo, L., Martin-Domingo, Luis
Publication Date
2022-09
Publication Place
-
Springer
Subject
Airport services, Collaborative networks, Content analysis, Sentiment analysis, Social media data mining, Term extraction, Topic modelling, User-generated content
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-303114843-9
Record ID
4ba77747-ed36-4ec8-85b8-6709a96e1106
Library Location
Aviation Management
Date
2022-09
Notes
Ministry of Education and Culture in Finland
Sample Text
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.
DOI
10.1007/978-3-031-14844-6_8
Cilt
662
