Exploiting user-generated content for service improvement: Case airport twitter data

Name: Exploiting user-generated content for service improvement: Case airport twitter data
Author: Aunimo, L., Martin-Domingo, Luis

İsim	Exploiting user-generated content for service improvement: Case airport twitter data
Yazar	Aunimo, L., Martin-Domingo, Luis
Basım Tarihi:	2022-09
Basım Yeri	- Springer
Konu	Airport services, Collaborative networks, Content analysis, Sentiment analysis, Social media data mining, Term extraction, Topic modelling, User-generated content
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-303114843-9
Kayıt Numarası	4ba77747-ed36-4ec8-85b8-6709a96e1106
Lokasyon	Aviation Management
Tarih	2022-09
Notlar	Ministry of Education and Culture in Finland
Örnek Metin	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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Exploiting user-generated content for service improvement: Case airport twitter data

Yazar Aunimo, L., Martin-Domingo, Luis

Basım Tarihi 2022-09

Basım Yeri - Springer

Konu Airport services, Collaborative networks, Content analysis, Sentiment analysis, Social media data mining, Term extraction, Topic modelling, User-generated content

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-303114843-9

Kayıt Numarası 4ba77747-ed36-4ec8-85b8-6709a96e1106

Lokasyon Aviation Management

Tarih 2022-09

Notlar Ministry of Education and Culture in Finland

Örnek Metin 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