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Attributes prediction from IoT consumer reviews in the hotel sectors using conventional neural network: deep learning techniques

Author

Listed:
  • Alaa Shoukry

    (King Saud University
    Workers University)

  • Fares Aldeek

    (King Saud University)

Abstract

The Internet of Things (IoT) plays an important role in helping the hotel industry increase customer satisfaction while maintaining affordable costs. IoT consumers review and rate the hotels online. The ratings are based on the Value, Apartment, Site, Sanitation, Front Desk, Facility, Professional Provision, Internet, and Packing. Traditional systems that predict hotel ratings with minimum accuracy create complexity through their analysis of the ratings. Thus, the effective deep learning techniques are used to analyze the reviews in order to help consumers choose better hotels. In this paper, different classification algorithms, such as convolutional neural network-based deep learning (CNN-DL), support vector machine network-based deep learning are applied to predict attributes. The system utilizes the TripAdvisor site, which is a well-known America dataset for examining system efficiency. The experimental results show that the CNN-DL algorithm has better classification accuracy and a lower error rate as compared to other algorithms.

Suggested Citation

  • Alaa Shoukry & Fares Aldeek, 2020. "Attributes prediction from IoT consumer reviews in the hotel sectors using conventional neural network: deep learning techniques," Electronic Commerce Research, Springer, vol. 20(2), pages 223-240, June.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:2:d:10.1007_s10660-019-09373-4
    DOI: 10.1007/s10660-019-09373-4
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    References listed on IDEAS

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    2. Zhang, Ye & Cole, Shu Tian, 2016. "Dimensions of lodging guest satisfaction among guests with mobility challenges: A mixed-method analysis of web-based texts," Tourism Management, Elsevier, vol. 53(C), pages 13-27.
    3. Lu, Weilin & Stepchenkova, Svetlana, 2012. "Ecotourism experiences reported online: Classification of satisfaction attributes," Tourism Management, Elsevier, vol. 33(3), pages 702-712.
    4. Chen, Li-Fei, 2012. "A novel approach to regression analysis for the classification of quality attributes in the Kano model: an empirical test in the food and beverage industry," Omega, Elsevier, vol. 40(5), pages 651-659.
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    Cited by:

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    2. Rishikesh Bhaiswar & N. Meenakshi & Deepak Chawla, 2021. "Evolution of Electronic Word of Mouth: A Systematic Literature Review Using Bibliometric Analysis of 20 Years (2000–2020)," FIIB Business Review, , vol. 10(3), pages 215-231, September.

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