Multiclass sentiment prediction of airport service online reviews using aspect-based sentimental analysis and machine learning

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dc.contributor.authorAlanazi, Mohammed Saad M.
dc.contributor.authorLi, Jun
dc.contributor.authorJenkins, Karl W.
dc.date.accessioned2024-03-18T11:51:19Z
dc.date.available2024-03-18T11:51:19Z
dc.date.issued2024-03-06
dc.description.abstractAirport service quality ratings found on social media such as Airline Quality and Google Maps offer invaluable insights for airport management to improve their quality of services. However, there is currently a lack of research analysing these reviews by airport services using sentimental analysis approaches. This research applies multiclass models based on Aspect-Based Sentimental Analysis to conduct a comprehensive analysis of travellers’ reviews, in which the major airport services are tagged by positive, negative, and non-existent sentiments. Seven airport services commonly utilised in previous studies are also introduced. Subsequently, various Deep Learning architectures and Machine Learning classification algorithms are developed, tested, and compared using data collected from Twitter, Google Maps, and Airline Quality, encompassing travellers’ feedback on airport service quality. The results show that the traditional Machine Learning algorithms such as the Random Forest algorithm outperform Deep Learning models in the multiclass prediction of airport service quality using travellers’ feedback. The findings of this study offer concrete justifications for utilising multiclass Machine Learning models to understand the travellers’ sentiments and therefore identify airport services required for improvement.en_UK
dc.identifier.citationAlanazi MS, Li J, Jenkins KW. (2024) Multiclass sentiment prediction of airport service online reviews using aspect-based sentimental analysis and machine learning. Mathematics, Volume 12, Issue 5, March 2024, Article number 781en_UK
dc.identifier.issn2227-7390
dc.identifier.urihttps://doi.org/10.3390/math12050781
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21020
dc.language.isoen_UKen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectairport service qualityen_UK
dc.subjectDeep Learningen_UK
dc.subjectTwitteren_UK
dc.subjectGoogle Mapsen_UK
dc.subjectAirline Qualityen_UK
dc.titleMulticlass sentiment prediction of airport service online reviews using aspect-based sentimental analysis and machine learningen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-03-02

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