With the rapid development of the Internet and travel market, the availability of online short-term rental rooms has emerged, making good use of limited room resources. In current research, only structured data of room characteristics, such as the location of the room, are considered in the price of the room. As unstructured textual data, online user’s reviews containing the emotional responses of users influence the price of online short-term rental rooms. In this research, user reviews are considered in a hedonic price regression model to improve the performance of regression. First, structured room characteristics are input to build a traditional hedonic price regression model. Then, fusion of emotion scores transformed from unstructured user reviews is input to build a fusion hedonic price regression model. Finally, the traditional model and the fusion model are compared statistically. Experimental results indicate that the fusion of user reviews can improve the performance of hedonic price regression model.

1. [1] B. Pan, T. MacLaurin, and J. C. Crotts, “Travel Blogs and the Implications for Destination Marketing,” J. of Travel Research, Vol.46, No.1, pp. 35-45, 2007.
2. [2] Q. Ye, R. Law, and B. Gu, “The Impact of Online User Reviews on Hotel Room Sales,” Int. J. of Hospitality Management, Vol.28, No.1, pp. 180-182, 2009.
3. [3] R. F. Muth, “Household production and consumer demand functions,” Econometrica: J. of the Econometric Society, Vol.34, No.3, pp. 699-708, 1966.
4. [4] B. G. Edelman and M. Luca, “Digital Discrimination: The Case of Airbnb.com,” Harvard Business School Working Paper, No.14-054, 2014.
5. [5] H. S. Shin, D. M. Hanssens, and B. Gajula, “The impact of Positive vs negative online buzz on retail Prices,” UCLA Working Paper, 2008.
6. [6] J. Goldenberg, B. Libai, and E. Muller, “Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth,” Marketing Letters, Vol.12, No.3, pp. 211-223, 2001.
7. [7] D. Stokes and W. Lomax, “Taking Control of Word of Mouth Marketing: The Case of an Entrepreneurial Hotelier,” J. of Small Business and Enterprise Development, Vol.9, No.4, pp. 349-357, 2002.
8. [8] M. Aggett, “What has influenced growth in the UK’s boutique hotel sector?,” Int. J. of Contemporary Hospitality Management, Vol.19, No.2, pp. 169-177, 2007.
9. [9] C. C. Lee and C. Hu, “Analyzing Hotel Customers’ E-Complaints from an Internet Complaint Forum,” J. of Travel & Tourism Marketing, Vol.17, No.2-3, pp. 167-181, 2004.
10. [10] M. S. Chaves, R. Gomes, and C. Pedron, “Analyzing reviews in the Web 2.0: Small and medium hotels in Portugal,” Tourism Management, No.33, Vol.5, pp. 1286-1287, 2012.
11. [11] J. Wong and R. Law, “Analyzing the intention to purchase on hotel websites: A study of travelers to Hong Kong,” Int. J. of Hospitality Management, Vol.24, No.3, pp. 311-329, 2005.
12. [12] S. Zhuo and Y. Feng, “Empirical Study of Online Consumer Review Helpfulness Determinants – Based on Hotel Review Data of TripAdvisor. Com,” J. of Modern Information, No.04, pp. 52-56+74, 2015.
13. [13] I. E. Vermeulen and D. Seegers, “Tried and tested: The impact of online hotel reviews on consumer consideration,” Tourism Management, Vol.30, No.1, pp. 123-127, 2009.
14. [14] C. L. Zitnick and T. Kanade, “Maximum Entropy for Collaborative Filtering,” 20th Conf. of Uncertainty in Artificial Intelligence, pp. 636-643, 2004.
15. [15] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” The J. of Machine Learning Research, Vol.3, pp. 993-1022, 2003.
16. [16] T. L. Griffiths and M. Steyvers, “Finding scientific topics,” Proc. of the National Academy of Sciences, Vol.101, No.1, pp. 5228-5235, 2004.
17. [17] K.-S. Shi, H.-T. Liu, Y.-C. Bai et al., “Text Clustering Method with Improved Fitness Function and Cosine Similarity Measure,” J. of University of Electronic Science and Technology of China, Vol.42, No.4, pp. 621-624, 2013.