Emotional characteristics and theme mining of star-rated hotels from the perspective of social sensing: a case study of Nanchang City, China
Tóm tắt
Mining hotel social sensing data and analyzing its spatial and temporal characteristics can provide decision support for hotel managers. Present research on this topic is limited to the overall hotel industry and text mining. Here, we first obtain POI and reviews for star-rated hotels in Nanchang from 2018 to 2021. Secondly, the hotel POI (Point of Interest) is combined with the sentiment value of customer reviews. Finally, comparative analysis and topic mining of Spatio-temporal aspects of text reviews of different star-rated hotels are conducted using sentiment analysis, spatial analysis, and thematic social network analysis. Results show that: (1) Hotel star rating and hotel review sentiment value are significantly positively correlated. The seasonal trends of different star rating hotel sentiment values are similar, but are highest in summer and lower in autumn; (2) The highest sentiment value is seen for friends’ outings and the lowest is for business trips; (3) Customer reviews of star-rated hotels focus on three aspects: facilities, service, and location. Three-star hotels focus on the stay experience, while four-star hotels focus on the breakfast situation. Exploring hotel social sensing data can intuitively illustrate hotel selection’s behavioral patterns and spatial-temporal characteristics. The methods of this study can expand the application of social sensing data in different fields, such as the tourism and restaurant industries.
Tài liệu tham khảo
Acheampong, F. A., Nunoo-Mensah, H., & Wenyu, C. (2021). Transformer models for text-based emotion detection: a review of best-based approaches. Artificial Intelligence Review, 54(8), 5789–5829. https://doi.org/10.1007/s10462-021-09958-2
Berrar, D. (2014). An empirical evaluation of ranking measures concerning robustness to noise. AI Access Foundation. https://doi.org/10.1613/jair.4136
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993–1022. https://sci-hub.wf/10.1162/jmlr.2003.3.4-5.993
Chang, Y. C., Ku, C. H., & Chen, C. H. (2017). Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management, 48, 263–279. https://doi.org/10.1016/j.ijinfomgt.2017.11.001
Choi, H. J., & Park, C. H. (2019). Emerging topic detection in a twitter stream based on high utility pattern mining. Expert Systems with Applications, 115, 27–36. https://doi.org/10.1016/j.eswa.2018.07.051
Dong, X., & Xu, S. (2020). Spatial evolution characteristics of urban and rural settlements in Inner Mongolia. Arabian Journal of Geosciences, 13(22), 1–11. https://doi.org/10.1007/s12517-020-06167-z
Fang, Y., Mao, J., Liu, Q., & Huang, J. (2021). Exploratory space data analysis of spatial patterns of large-scale retail commercial facilities: The case of Gulou District, Nanjing, China. Frontiers of Architectural Research, 10(1), 17–32. https://doi.org/10.1016/j.foar.2020.02.002
Goloshchapova, I., Poon, S. H., Pritchard, M., & Reed, P. (2019). Corporate social responsibility reports topic analysis and big data approach. The European Journal of Finance, 25(17), 1637–1654. https://doi.org/10.1016/j.foar.2020.02.002
Han, C., & Chiu, & Cheng. (2020). Application of support vector machine (SVM) in the sentiment analysis of the twitter dataset. Applied Sciences, 10(3), 1125. https://doi.org/10.3390/app10031125
He, W., Xie, H., & Feng, G. (2018). Review on latent Dirichlet allocation model. Journal of Information Resources Management. https://doi.org/10.1177/1729881420904213
Jia, F., & Chen, C. C. (2020). Emotional characteristics and time series analysis of internet public opinion participants based on emotional feature words. International Journal of Advanced Robotic Systems, 17(1), 1729881420904213. https://doi.org/10.1177/1729881420904213
Li, H., Liu, Y., Tan, C. W., & Hu, F. (2020). Comprehending customer satisfaction with hotels: Data analysis of consumer-generated reviews. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-06-2019-0581
Luo, X., & Yi, Y. (2019). Topic-specific emotion mining model for online comments. Future Internet, 11(3), 79. https://doi.org/10.3390/fi11030079
Maomao, C., Meiyu, P., & Weijun, W. (2021). A cross-platform comparative study of reviews on sharing accommodation and hotels reservation platform: Combined with LDA-SNA and sentiment analysis. Library and Information Service, 65(2), 107.
Muhammad, P. F., Kusumaningrum, R., & Wibowo, A. (2021). Sentiment analysis using word2vec and long short-term memory (lstm) for Indonesian hotel reviews. Procedia Computer Science, 179(6), 728–735. https://doi.org/10.1016/j.procs.2021.01.061
Rao, Y., Lei, J., Wenyin, L., Li, Q., & Chen, M. (2014). Building emotional dictionary for sentiment analysis of online news. World Wide Web, 17(4), 723–742. https://doi.org/10.1007/s11280-013-0221-9
Reagan, A. J., Tivnan, B., Williams, J. R., Danforth, C. M., & Dodds, P. S. (2017). Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs. Ep Data ence, 6(1), 28. https://doi.org/10.48550/arXiv.1512.00531
Rusanov, A., Miotto, R., & Weng, C. (2018). Trends in anesthesiology research: A machine learning approach to theme discovery and summarization. JAMIA Open, 1(2), 283–293. https://doi.org/10.1093/jamiaopen/ooy009
Stringam, B. B., & Gerdes, J. (2010). An analysis of word-of-mouse ratings and guest comments of online hotel distribution sites. Journal of Hospitality Marketing & Management, 19(7), 773–796. https://doi.org/10.1080/19368623.2010.508009
Tan, X., Zhuang, M., Lu, X., & Mao, T. (2021a). An analysis of the emotional evolution of large-scale internet public opinion events based on the BERT-LDA hybrid model. IEEE Access, 9, 15860–15871. https://doi.org/10.1109/ACCESS.2021.3052566
Tan, X., Zhuang, M., Lu, X., & Mao, T. (2021b). An analysis of the emotional evolution of large-scale internet public opinion events based on the BERT-LDA hybrid model. IEEE Access, 9, 15860–15871. https://doi.org/10.1109/ACCESS.2021.3052566
Tang, J., Hao, S., & Qu, W. (2019). Sentiment analysis of online Chinese comments based on statistical learning combined with pattern matching. Concurrency and Computation: Practice and Experience, 31(10), e4765. https://doi.org/10.1002/cpe.4765
Tao, Y., Zhang, F., Shi, C., & Chen, Y. (2019). Social media data-based sentiment analysis of tourists’ air quality perceptions. Sustainability, 11(18), 5070. https://doi.org/10.3390/su11185070
Valdivia, A., Luzon, M. V., & Herrera, F. (2017). Sentiment analysis in TripAdvisor. IEEE Intelligent Systems, 32(4), 72–77. https://doi.org/10.1109/MIS.2017.3121555
Wang, F., Wang, Z., Wang, S., & Li, Z. (2014). Exploiting description knowledge for keyphrase extraction. In Pacific Rim International Conference on Artificial Intelligence (pp. 130–142). Springer. https://doi.org/10.1007/978-3-319-13560-1_11
Wang, J., Zhao, Z., Liu, Y., & Guo, Y. (2021). Research on the role of influencing factors on hotel customer satisfaction based on BP neural network and text mining. Information, 12(3), 99. https://doi.org/10.3390/info12030099
Wang, L., Wang, X. K., Peng, J. J., & Wang, J. Q. (2020). The differences in hotel selection among various types of travelers: A comparative analysis with a useful bounded rationality behavioral decision support model. Tourism Management, 76(Feb.), 103961.1-103961.16. https://doi.org/10.1016/j.tourman.2019.103961
Yang, K., Lee, D., Whang, T., Lee, S., & Lim, H. (2019). Emotionx-ku: Bert-max based contextual emotion classifier. arXiv preprint arXiv:1906.11565. https://doi.org/10.48550/arXiv.1906.11565
Yen, C., & Tang, C. (2018). The effects of hotel attribute performance on electronic word-of-mouth (new) behaviors. International Journal of Hospitality Management, 76, 9–18. https://doi.org/10.1016/j.ijhm.2018.03.006
Zhang, C., Ma, X., Zhou, Y., & Guo, R. (2021). Analysis of public opinion evolution in COVID-19 pandemic from a perspective of sentiment variation. J Geo-Inf Sci, 23(02), 341–350.
Zhang, H., Dong, J., Min, L., & Bi, P. (2020). A bert fine-tuning model for targeted sentiment analysis of Chinese online course reviews. International Journal on Artificial Intelligence Tools, 29(07n08), 2040018. https://doi.org/10.1142/S0218213020400187
Zhang, J., Lu, X., & Liu, D. (2021). Deriving customer preferences for hotels based on aspect-level sentiment analysis of online reviews. Electronic Commerce Research and Applications, 49, 101094. https://doi.org/10.1016/j.elerap.2021.101094
Zhao, F., Ren, X., Yang, S., Han, Q., Zhao, P., & Yang, X. (2020). Latent Dirichlet allocation model training with differential privacy. IEEE Transactions on Information Forensics and Security, 16, 1290–1305. https://doi.org/10.1109/TIFS.2020.3032021