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Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS

Author

Listed:
  • Himanshu Sharma

    (University of Delhi)

  • Abhishek Tandon

    (University of Delhi)

  • P. K. Kapur

    (Amity University)

  • Anu G. Aggarwal

    (University of Delhi)

Abstract

Many a times, customers regret their decision when they book a hotel room purely on the basis of price or the hotel images available online. The customers look for additional information to substantiate their decision and this has led to the popularity of the usage of online feedbacks provided by guests towards various aspects of the hotel services. This feedback more appropriately called the electronic word-of-mouth is provided either in terms of some rating or textual comments. The numerical ratings of various service aspects of the hotels posted by guests provide a comprehensive evaluation of their sentiments and assessments on a standardized scale. Studying these sentiments is necessary in order to understand the customer needs and identify the improvement areas for hoteliers. Customers consider various alternatives and gather relevant aspect information before booking a hotel room. This involves evaluating the hotel alternatives on the basis of more than one hotel characteristics. This demands application of multi criteria decision making approach for ranking of hotels. The paper proposes a hotel ranking model based on the aspect ratings accessed from Tripadvisor website. The aspects play the role of criteria consisting of service, cleanliness, value, sleep quality, room, and location. These ratings are classified into positive, neutral, and negative sentiments, which are transformed to Neutrosophic numbers and results in the formation of interval-valued Neutrosophic decision matrix. Also, since the aspect weights are completely unknown, a non-linear programming model called maximizing deviation method is employed. Lastly, the aspect weights and decision matrix are combined to perform the procedure required for applying technique for order preferences by similarity to ideal solution method for ranking five alternative hotels. Future studies may extend the present model for various product selection problems for which product feature ratings are available.

Suggested Citation

  • Himanshu Sharma & Abhishek Tandon & P. K. Kapur & Anu G. Aggarwal, 2019. "Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 973-983, October.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:5:d:10.1007_s13198-019-00827-4
    DOI: 10.1007/s13198-019-00827-4
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    References listed on IDEAS

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    1. Shawn Mankad & Hyunjeong Spring Han & Joel Goh & Srinagesh Gavirneni, 2016. "Understanding Online Hotel Reviews Through Automated Text Analysis," Post-Print hal-02311939, HAL.
    2. Himanshu Sharma & Anu G. Aggarwal, 2019. "Finding determinants of e-commerce success: a PLS-SEM approach," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 16(4), pages 453-471, March.
    3. San-Martín, Sonia & Prodanova, Jana & Jiménez, Nadia, 2015. "The impact of age in the generation of satisfaction and WOM in mobile shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 23(C), pages 1-8.
    4. Abhishek Tandon & Himanshu Sharma & Anu Gupta Aggarwal, 2019. "Assessing Travel Websites Based on Service Quality Attributes Under Intuitionistic Environment," International Journal of Knowledge-Based Organizations (IJKBO), IGI Global, vol. 9(1), pages 66-75, January.
    5. Athar Kharal, 2014. "A Neutrosophic Multi-Criteria Decision Making Method," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 143-162.
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    Cited by:

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    3. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    4. Song, Yongming & Li, Yanhong & Zhu, Hongli & Li, Guangxu, 2023. "A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    5. Abhishek Tandon & Aakash Aakash & Anu G. Aggarwal & P. K. Kapur, 2021. "Analyzing the impact of review recency on helpfulness through econometric modeling," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 104-111, February.
    6. Heidary Dahooie, Jalil & Raafat, Romina & Qorbani, Ali Reza & Daim, Tugrul, 2021. "An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Abhishek Tandon & Aakash Aakash & Anu G. Aggarwal & P. K. Kapur, 0. "Analyzing the impact of review recency on helpfulness through econometric modeling," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-8.

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