Performance analysis of deep approaches on airbnb sentiment reviews

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dc.authorid0000-0003-1606-4079en_US
dc.contributor.authorRaza, Muhammad Raheel
dc.contributor.authorHussain, Walayat
dc.contributor.authorVarol, Asaf
dc.date.accessioned2024-07-12T20:58:00Z
dc.date.available2024-07-12T20:58:00Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractConsumer reviews in the Airbnb marketplace are one of the key attributes to measure the quality of services and the main determinant of consumer rentals decisions. Such feedback can impact both a new and repeated consumer's choice decision. The way to manage poor reviews can help to save or damage the host's reputation. Sentiment analysis enables an Airbnb host to get an insight into the business, pinpoint degradation of the specific component of compound services and assist in managing it proactively. Multiple Deep Learning algorithms have been used for Natural Language Processing (NLP). For optimal sentiment management in the Airbnb marketplace, it is crucial to identify the right algorithm. The paper uses multiple Deep Learning algorithms to identify different aspects of guest reviews and analyze their accuracies. The paper uses four accuracy measurement benchmarks – Precision, Recall, F1-score and Support to analyze results. The analysis shows that the GRU method achieves the best results with the highest classification metrics values as compared to RNN and LSTM.en_US
dc.identifier.citationRaza, M.R., Hussain, W. and Varol, A. (2022). Performance analysis of deep approaches on airbnb sentiment reviews. 10th International Symposium on Digital Forensics and Security (ISDFS), p.1-5.en_US
dc.identifier.endpage5en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12415/3119
dc.institutionauthorVarol, Asaf
dc.language.isoenen_US
dc.relation.ispartof10th International Symposium on Digital Forensics and Security (ISDFS)en_US
dc.relation.isversionof10.1109/ISDFS55398en_US
dc.relation.publicationcategoryUluslararası Konferans Öğesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKY00004
dc.subjectDeep learningen_US
dc.subjectSentiment analysisen_US
dc.subjectRNNen_US
dc.subjectLSTMen_US
dc.subjectGRUen_US
dc.subjectAirbnb reviewsen_US
dc.titlePerformance analysis of deep approaches on airbnb sentiment reviewsen_US
dc.typeArticle
dspace.entity.typePublication

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