Leveraging transformer-based language models for enhanced service insight in tourism
dc.authorid | 0009-0008-5993-6336 | |
dc.authorid | 0000-0003-3903-7356 | |
dc.authorid | 0000-0001-5106-0044 | |
dc.contributor.author | Er, Aleyna | en_US |
dc.contributor.author | Özçelik, Şuayb Talha | en_US |
dc.contributor.author | Yöndem, Meltem Turhan | en_US |
dc.date.accessioned | 2024-02-20T17:13:07Z | |
dc.date.available | 2024-02-20T17:13:07Z | |
dc.date.issued | 2023-12-22 | |
dc.department | Işık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.department | Işık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering | en_US |
dc.description.abstract | Customer feedback is a valuable resource for enhancing customer experience and identifying areas that require improvement. Utilizing user insights allows a tourism company to identify and address problematic points in its service delivery, provide feedback to partner companies regarding their product offerings, and even reconsider agreements by incorporating these opinions when curating their product portfolio. Setur implemented a systematic approach to collecting customer feedback by distributing "after-stay surveys'' to its customers via email following the completion of the agency services provided. Guest answers to open-ended questions that gather opinions about travel experience are analyzed by four tasks: user intention for answering, the sentiment of the review, subjects touched upon, and whom it concerned. For these tasks, transformer-based natural language processing (NLP) models BERT, DistilBERT, RoBERTa, and Electra are fine-Tuned to classify customer reviews. Based on the test results, it is observed that best practices could be gathered using Bert. In addition, we showed that different insights can be obtained from text comments made for two hotels in Aydin, Turkiye. Some users made complaints using neutral sentences. In some cases, people gave high scores to the numerical rating questions, but their open-ended questions could have a negative meaning. | en_US |
dc.description.version | Publisher's Version | en_US |
dc.identifier.citation | Er, A. & Özçelik, Ş. T. (2023). Leveraging transformer-based language models for enhanced service insight in tourism. Paper presented at the 4th International Informatics and Software Engineering Conference (IISEC), 1-6. doi:10.1109/IISEC59749.2023.10391041 | en_US |
dc.identifier.doi | 10.1109/IISEC59749.2023.10391041 | |
dc.identifier.endpage | 6 | |
dc.identifier.isbn | 9798350318036 | |
dc.identifier.isbn | 9798350318043 | |
dc.identifier.scopus | 2-s2.0-85184654784 | |
dc.identifier.scopusquality | N/A | |
dc.identifier.startpage | 1 | |
dc.identifier.uri | https://hdl.handle.net/11729/5904 | |
dc.identifier.uri | http://dx.doi.org/10.1109/IISEC59749.2023.10391041 | |
dc.indekslendigikaynak | Scopus | en_US |
dc.institutionauthor | Özçelik, Şuayb Talha | en_US |
dc.institutionauthorid | 0000-0003-3903-7356 | |
dc.language.iso | en | en_US |
dc.peerreviewed | Yes | en_US |
dc.publicationstatus | Published | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 4th International Informatics and Software Engineering Conference (IISEC) | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - İdari Personel ve Öğrenci | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | BERT | en_US |
dc.subject | DistilBERT | en_US |
dc.subject | Electra | en_US |
dc.subject | RoBERTa | en_US |
dc.subject | Tourism | en_US |
dc.subject | Natural language processing systems | en_US |
dc.subject | Customer experience | en_US |
dc.subject | Customer feedback | en_US |
dc.subject | ITS Services | en_US |
dc.subject | Language model | en_US |
dc.subject | Open-ended questions | en_US |
dc.subject | Service delivery | en_US |
dc.title | Leveraging transformer-based language models for enhanced service insight in tourism | en_US |
dc.type | Conference Object | en_US |
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