Inversini, 2009, Cultural destination usability: the case of visit bath, 319
Litvin, 2008, Electronic word-of-mouth in hospitality and tourism management, Tourism Manag., 29, 458, 10.1016/j.tourman.2007.05.011
Marchiori, 2011, The online reputation construct: does it matter for the tourism domain? A literature review on destinations' online reputation, Inform. Technol. Tourism, 13, 139, 10.3727/109830512X13283928066715
Kushcheva, 2022, Monitoring online reputation of tourist destinations in Finland, 9442, 10.21125/inted.2022.2451
Arumugam, 2023, Exploring the factors influencing heritage tourism development: a model development, 15, 11986
Dowling, 2001
A. Inversini, E. Marchiori, C. Dedekind, and L. Cantoni, “Applying a conceptual framework to analyze online reputation of tourism destinations,” 2010.
Cioppi, 2019, Online presence, visibility and reputation: a systematic literature review in management studies, J. Res. Interac. Marketing, 10.1108/JRIM-11-2018-0139
Cillo, 2021, Niche tourism destinations’ online reputation management and competitiveness in big data era: evidence from three Italian cases, Curr. Issues Tourism, 24, 177, 10.1080/13683500.2019.1608918
Zhu, 2020, Sentiment and guest satisfaction with peer-to-peer accommodation: when are online ratings more trustworthy?, Int. J. Hosp. Manag., 86, 10.1016/j.ijhm.2019.102369
Crisci, 2017, Predicting TV programme audience by using Twitter-based metrics, Multimedia Tools Applic., 1
Cenni, 2017, Twitter vigilance: a multi-user platform for cross-domain Twitter data analytics, NLP and sentiment analysis
Chauhan, 2017, Prediction of places of visit using tweets, Knowl. Inf. Syst, 50, 145, 10.1007/s10115-016-0936-x
Hu, 2022, Tourism demand forecasting using tourist-generated online review data, Tourism Manag, 90, 10.1016/j.tourman.2022.104490
Box, 2015
Xiang, 2017, A comparative analysis of major online review platforms: implications for social media analytics in hospitality and tourism, Tourism Manag., 58
Chu, 2022, Language interpretation in travel guidance platform: text mining and sentiment analysis of TripAdvisor reviews, Front. Psychol., 13, 10.3389/fpsyg.2022.1029945
Puh, 2023, Predicting sentiment and rating of tourist reviews using machine learning, J. Hospital. Tourism Insights, 6, 1188, 10.1108/JHTI-02-2022-0078
Chen, 2016, XGBoost
Lea, 2016, Temporal convolutional networks: a unified approach to action segmentation, arXiv
Lim, 2019, Temporal fusion transformers for interpretable multi-horizon time-series forecasting, arXiv
Liu, 2019, DeepCount: crowd counting with WiFi via deep learning, arXiv preprint
Wu, 2018, Multipoint infrared laser-based detection and tracking for people counting, Neural. Comput. Appl., 29, 1405, 10.1007/s00521-017-3196-0
Collini, 2023, Flexible thermal camera solution for smart city people detection and counting
Ivanovski, 2018, Time series forecasting using a moving average model for extrapolation of number of tourist, UTMS J. Economics, 9
Chang, 2017, Apply deep learning neural network to forecast number of tourists, 259
Laaroussi, 2020, Deep Learning Framework for Forecasting Tourism Demand, 1
Chen, 2023, Identifying the role of media discourse in tourism demand forecasting, Curr. Issues Tourism, 1
Li, 2020, Forecasting tourism demand with multisource big data, Annals Tourism Res., 83, 10.1016/j.annals.2020.102912
McCulloch, 1943, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys., 5, 115, 10.1007/BF02478259
Cortes, 1995, Support-vector networks, Mach. Learning, 20, 273, 10.1007/BF00994018
Cho, 2014, Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv Preprint arXiv
Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735
Phan, 2020, A comparative analysis of XGBoost and temporal convolutional network models for wind power forecasting, 416
Hu, 2021, Stock price prediction based on temporal fusion transformer, 60
Chugh, 2020, Bangkok tours and activities data analysis via user-generated content, 98
Asteriou, 2011, ARIMA models and the Box–Jenkins methodology, Appl. Econometrics, 2, 265
Ho, 1995, Random decision forests, 1, 278
H. Song and L. Han, “Predicting tourist demand using big data,” 2017, pp. 13–29. doi:10.1007/978-3-319-44263-1_2.
Miah, 2017, A big data analytics method for tourist behaviour analysis, Inform. Manag, 54, 771, 10.1016/j.im.2016.11.011
De la Calle-Vaquero, 2021, Urban planning regulations for tourism in the context of overtourism. applications in historic centres, Sustainability, 13, 70, 10.3390/su13010070
Ribeiro, 2016, Why should i trust you?”: explaining the predictions of any classifier, arXiv
Tokarchuk, 2022, How much is too much? Estimating tourism carrying capacity in urban context using sentiment analysis, Tourism Manag., 91, 10.1016/j.tourman.2022.104522
McCool, 2001, Tourism carrying capacity: tempting fantasy or useful reality?, J. Sustain. Tourism, 9, 372, 10.1080/09669580108667409
Ogunleye, 2019, XGBoost model for chronic kidney disease diagnosis, IEEE/ACM Trans. Comput. Biol. Bioinf., 17, 2131, 10.1109/TCBB.2019.2911071
Ramdani, 2022, The simplicity of XGBoost algorithm versus the complexity of random forest, support vector machine, and neural networks algorithms in urban forest classification, F1000Research, 11, 1069, 10.12688/f1000research.124604.1
Zou, 2022, Optimized XGBoost model with small dataset for predicting relative density of Ti-6Al-4V parts manufactured by selective laser melting, Materials, 15, 5298, 10.3390/ma15155298
Vainio, 2017, Highly tweeted science articles: who tweets them? An analysis of Twitter user profile descriptions, Scientometrics, 112, 345, 10.1007/s11192-017-2368-0
AmArAl, 2014, User-generated content: tourists’ profiles on Tripadvisor, Int. J. Strategic Innovative Market., 1, 137
Arefieva, 2022, TourBERT: a pretrained language model for the tourism industry, arXiv preprint arXiv:2201.07449.
Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2018). BERT: pre-training of deep bidirectional transformers for language understanding.
Granger, 1969, Investigating causal relations by econometric models and cross-spectral, Methods Econom, 37, 424
De Luca, 2020, Sustainable cultural heritage planning and management of overtourism in art cities: lessons from atlas world heritage, Sustainability, 12, 3929, 10.3390/su12093929