Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model. (August 2022)

• Record Type: Journal Article
• Authors: Rajesh, Sudha; Abd Algani, Yousef Methkal; Al Ansari, Mohammed Saleh; Balachander, Bhuvaneswari; Raj, Roop; Muda, Iskandar; Kiran Bala, B.; Balaji, S.
• Is Part Of: Measurement. Volume 22(2022)
• Publication Date: 2022-08
• Subjects: Internet of things -- Deep learning -- Convolutional neural network -- Hotel sector -- Sentiments -- Predictions -- Internet reviews; Detectors -- Periodicals; Measurement -- Periodicals; 530.7
• DOI: 10.1016/j.measen.2022.100384
• Languages: English
• ISSNs: 2665-9174

Abstract

Abstract: Tourism and the hotel business have benefited greatly from the use of digital social networking. Using social big data research, the application of deep learning seems to have been beneficial in a marketing strategies and customer preference estimate. Recognizing human psychology, which is critical to industrial success, has benefited greatly from digital technology and social media. The Internet of Things (IoT) provides a chance for a hotel sector to improve the customer experience although lowering operational expenses. The ratings are determined by the following factors: Value, Apartment, Location, Hygiene, Front Office, Facilities, Professional Service, Internet, and Parking. Traditional techniques which anticipate hotel evaluations through minimal precision add difficulty to the rating assessment. As a result, efficient deep learning algorithms are employed to evaluate reviews designed to help consumers in selecting better hotels. To predict qualities, multiple classification techniques, including convolutional neural network-based deep learning (CNN-DL) and support vector machine (SVM) network-based deep learning, were used in this research. The system examines system efficiency by using the TripAdvisor website, this is a well American database. The research results reveal that the CNN-DL method outperforms another method in terms of classification efficiency and failure rate.The graphical results could also be utilized to enhance the effectiveness of the suggested model and offer insights into response tactics, demonstrating the study's academic and conceptual achievements. Although it is feasible to conclude from such research that the possibility of IoT within the hotel industry has not yet been fully investigated, as researchers commonly speculate on using IoT for implementations that might quickly be of involvement to a hotel sector, but refuse to recognize that possibility as a massive market.