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dc.contributor.authorMartin-Domingo, Luis
dc.date.accessioned2020-07-07T07:33:53Z
dc.date.available2020-07-07T07:33:53Z
dc.date.issued2019-07
dc.identifier.issn0969-6997en_US
dc.identifier.urihttp://hdl.handle.net/10679/6718
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S0969699719300079
dc.description.abstractUser 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.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Air Transport Management
dc.rightsrestrictedAccess
dc.titleSocial media as a resource for sentiment analysis of Airport Service Quality (ASQ)en_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0003-2052-5712 & YÖK ID 197484) Martin, Luis
dc.contributor.ozuauthorMartin-Domingo, Luis
dc.identifier.volume78en_US
dc.identifier.startpage106en_US
dc.identifier.endpage115en_US
dc.identifier.wosWOS:000473837500013
dc.identifier.doi10.1016/j.jairtraman.2019.01.004en_US
dc.subject.keywordsAirporten_US
dc.subject.keywordsServiceen_US
dc.subject.keywordsQualityen_US
dc.subject.keywordsTwitteren_US
dc.subject.keywordsSentiment analysisen_US
dc.identifier.scopusSCOPUS:2-s2.0-85060097357
dc.contributor.authorMale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institution Academic Staff


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