Performance Analysis of Deep Approaches on Airbnb Sentiment Reviews

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dc.contributor.authorRaza, M.R.
dc.contributor.authorHussain, W.
dc.contributor.authorVarol, A.
dc.date.accessioned2024-07-12T21:40:43Z
dc.date.available2024-07-12T21:40:43Z
dc.date.issued2022en_US
dc.department[Belirlenecek]en_US
dc.descriptionIEEE Societyen_US
dc.description10th International Symposium on Digital Forensics and Security, ISDFS 2022 -- 6 June 2022 through 7 June 2022 -- -- 180285en_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. © 2022 IEEE.en_US
dc.identifier.doi10.1109/ISDFS55398.2022.9800816
dc.identifier.isbn9.78167E+12
dc.identifier.scopus2-s2.0-85134198096en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ISDFS55398.2022.9800816
dc.identifier.urihttps://hdl.handle.net/20.500.12415/7456
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof10th International Symposium on Digital Forensics and Security, ISDFS 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY08800
dc.subjectAirbnb Reviewsen_US
dc.subjectDeep Learningen_US
dc.subjectGruen_US
dc.subjectLstmen_US
dc.subjectRnnen_US
dc.subjectSentiment Analysisen_US
dc.titlePerformance Analysis of Deep Approaches on Airbnb Sentiment Reviewsen_US
dc.typeConference Object
dspace.entity.typePublication

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