Analyzing Big Social Data for Evaluating Environment-Friendly Tourism in Turkey

Abstract

Tourism in Türkiye is fundamentally important for both the Turkish economy and travelers. Green tourism has gained increasing attention in the last few years. Analyzing big social data for evaluating environment-friendly tourism in Türkiye is important to gain an understanding of the factors impacting travelers' intention to echo-friendly hotels. To meet the goal of the study, the data was retrieved from the Tripadvisor website using a crawling technique. Machine learning techniques, particularly Latent Dirichlet Allocation (LDA), were utilized to discover satisfaction dimensions from the user-generated content. The k-means clustering approach was deployed for data segmentation. Finally, the online reviews classification model was trained and compared using Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The obtained results reveal several important dimensions that impact tourists' experience.

Keywords

• LDA topic model
• machine learning
• text mining
• segmentation
• online customers’ reviews

References

1. Acampora, A., Lucchetti, M. C., Merli, R., & Ali, F. 2022. The theoretical development and research methodology in green hotels research: A systematic literature review. Journal of Hospitality and Tourism Management, 51, 512-528.
2. Afrizal, A. D., Rakhmawati, N. A., & Tjahyanto, A. 2019. New filtering scheme based on term weighting to improve object based opinion mining on tourism product reviews. Procedia Computer Science, 161, 805-812.
3. Alzate, M., Arce-Urriza, M., & Cebollada, J. 2022. Mining the text of online consumer reviews to analyze brand image and brand positioning. Journal of Retailing and Consumer Services, 67, 102989.
4. Arulraj, T., & Daisy, S. J. S. 2021. Mining online review for predicting sales performance. Materials Today: Proceedings, 47, 93-99.
5. Association, G. H. 2008. What are green hotels. Retrieved May, 10, 2008.
6. Bauer, T., Jago, L., & Wise, B. 1993. The changing demand for hotel facilities in the Asia Pacific region. International Journal of Hospitality Management, 12(4), 313-322.
7. Berezan, O., Raab, C., Yoo, M., & Love, C. 2013. Sustainable hotel practices and nationality: The impact on guest satisfaction and guest intention to return. International Journal of Hospitality Management, 34, 227-233.
8. Bian, Y., Ye, R., Zhang, J., & Yan, X. 2022. Customer preference identification from hotel online reviews: A neural network based fine-grained sentiment analysis. Computers & Industrial Engineering, 108648.

9. Blei, D. M., Ng, A. Y., & Jordan, M. I. 2003. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
10. Bozkurt, F., Çoban, Ö., Baturalp Günay, F., & Yücel Altay, Ş. 2019. High performance twitter sentiment analysis using CUDA based distance kernel on GPUs. Tehnički vjesnik, 26(5), 1218-1227.
11. Chen, Q., Hu, M., He, Y., Lin, I., & Mattila, A. S. 2022. Understanding guests’ evaluation of green hotels: The interplay between willingness to sacrifice for the environment and intent vs. quality-based market signals. International Journal of Hospitality Management, 104, 103229.
12. Chen, W., Yeo, C. K., Lau, C. T., & Lee, B. S. 2018. Leveraging social media news to predict stock index movement using RNN-boost. Data & Knowledge Engineering, 118, 14-24.
13. Chimmula, V. K. R., & Zhang, L. 2020. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals, 135, 109864.
14. D’Alessandro, F. 2016. Green Building for a Green Tourism. A new model of eco-friendly agritourism. Agriculture and agricultural science procedia, 8, 201-210.
15. De Palma, A., Criado, C. O., & Randrianarisoa, L. M. 2018. When Hotelling meets Vickrey. Service timing and spatial asymmetry in the airline industry. Journal of Urban Economics, 105, 88-106.
16. Fan, H., Gao, W., & Han, B. 2022. How does (im) balanced acceptance of robots between customers and frontline employees affect hotels’ service quality? Computers in Human Behavior, 133, 107287.
17. Filimonau, V., Matute, J., Mika, M., Kubal-Czerwińska, M., Krzesiwo, K., & Pawłowska-Legwand, A. 2022. Predictors of patronage intentions towards ‘green’hotels in an emerging tourism market. International Journal of Hospitality Management, 103, 103221.
18. Godnov, U., & Redek, T. 2016. Application of text mining in tourism: case of Croatia. Annals of Tourism Research, 58, 162-166.
19. Han, H., Lee, J.-S., Trang, H. L. T., & Kim, W. 2018. Water conservation and waste reduction management for increasing guest loyalty and green hotel practices. International Journal of Hospitality Management, 75, 58-66.
20. Harif, M. A. A. M., Nawaz, M., & Hameed, W. U. 2022. The role of open innovation, hotel service quality and marketing strategy in hotel business performance. Heliyon, e10441.
21. Hochreiter, S., & Schmidhuber, J. 1996. LSTM can solve hard long time lag problems. Advances in neural information processing systems, 9.
22. Hochreiter, S., & Schmidhuber, J. 1997. Long short-term memory. Neural computation, 9(8), 1735-1780.
23. Huang, J., Guo, Y., Wang, C., & Yan, L. 2019. You touched it and I’m relieved! The effect of online review’s tactile cues on consumer’s purchase intention. Journal of Contemporary Marketing Science.
24. Huang, S., Zhang, J., Yang, C., Gu, Q., Li, M., & Wang, W. 2022. The interval grey QFD method for new product development: Integrate with LDA topic model to analyze online reviews. Engineering Applications of Artificial Intelligence, 114, 105213.
25. Jung, M., Lee, H., & Tani, J. 2018. Adaptive detrending to accelerate convolutional gated recurrent unit training for contextual video recognition. Neural Networks, 105, 356-370.
26. Khaldi, R., El Afia, A., Chiheb, R., & Tabik, S. 2023. What is the best RNN-cell structure to forecast each time series behavior? Expert Systems with Applications, 215, 119140.
27. Kim, J.-Y., Hlee, S., & Joun, Y. 2016. Green practices of the hotel industry: Analysis through the windows of smart tourism system. International Journal of Information Management, 36(6), 1340-1349.
28. Liang, M., & Niu, T. 2022. Research on Text Classification Techniques Based on Improved TF-IDF Algorithm and LSTM Inputs. Procedia Computer Science, 208, 460-470.
29. Ma, G., Ma, J., Li, H., Wang, Y., Wang, Z., & Zhang, B. 2022. Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce. Energy Policy, 165, 112960.
30. Magoulas, R., & Swoyer, S. 2020. AI Adoption in the Enterprise. Beijing: O´ Reilly. Recuperado de http://www. oreilly. com/data/free/ai ….
31. Merli, R., Preziosi, M., Acampora, A., & Ali, F. 2019. Why should hotels go green? Insights from guests experience in green hotels. International Journal of Hospitality Management, 81, 169-179.
32. Moirangthem, D. S., & Lee, M. 2021. Hierarchical and lateral multiple timescales gated recurrent units with pre-trained encoder for long text classification. Expert Systems with Applications, 165, 113898.
33. Nilashi, M., Abumalloh, R. A., Alghamdi, A., Minaei-Bidgoli, B., Alsulami, A. A., Thanoon, M., Asadi, S., & Samad, S. 2021. What is the impact of service quality on customers’ satisfaction during COVID-19 outbreak? New findings from online reviews analysis. Telematics and Informatics, 64, 101693.
34. Nilashi, M., Abumalloh, R. A., Almulihi, A., Alrizq, M., Alghamdi, A., Ismail, M. Y., Bashar, A., Zogaan, W. A., & Asadi, S. 2021. Big social data analysis for impact of food quality on travelers’ satisfaction in eco-friendly hotels. ICT Express.
35. Nilashi, M., Abumalloh, R. A., Minaei-Bidgoli, B., Zogaan, W. A., Alhargan, A., Mohd, S., Azhar, S. N. F. S., Asadi, S., & Samad, S. 2022. Revealing travellers’ satisfaction during COVID-19 outbreak: moderating role of service quality. Journal of Retailing and Consumer Services, 64, 102783.
36. Nilashi, M., Minaei-Bidgoli, B., Alrizq, M., Alghamdi, A., Alsulami, A. A., Samad, S., & Mohd, S. 2021. An analytical approach for big social data analysis for customer decision-making in eco-friendly hotels. Expert Systems with Applications, 186, 115722.
37. Park, S., Lundeen, E., & Blanck, H. 2016. Knowledge of Health Conditions Related to Drinking Sugar-Sweetened Beverage and Sugar-Sweetened Beverage Intake Among US Adults. Journal of Nutrition Education and Behavior, 48(7), S98.
38. Perramon, J., Oliveras-Villanueva, M., & Llach, J. 2022. Impact of service quality and environmental practices on hotel companies: An empirical approach. International Journal of Hospitality Management, 107, 103307.
39. Prihayati, Y., & Veriasa, T. O. 2021. Developing green tourism to create the sustainable landscape: evidence from Community-based Coffee Tourism (CbCT) in Puncak, Bogor, Indonesia. IOP Conference Series: Earth and Environmental Science,
40. Rita, P., Moro, S., & Cavalcanti, G. 2022. The impact of COVID-19 on tourism: Analysis of online reviews in the airlines sector. Journal of Air Transport Management, 104, 102277.
41. Shaheen, M., Zeba, F., Chatterjee, N., & Krishnankutty, R. 2019. Engaging customers through credible and useful reviews: the role of online trust. Young Consumers.
42. Sim, Y., Lee, S. K., & Sutherland, I. 2021. The impact of latent topic valence of online reviews on purchase intention for the accommodation industry. Tourism Management Perspectives, 40, 100903.
43. Streimikiene, D., Svagzdiene, B., Jasinskas, E., & Simanavicius, A. 2021. Sustainable tourism development and competitiveness: The systematic literature review. Sustainable development, 29(1), 259-271.
44. Tian, Y., & Zhang, Y. 2022. Pricing of crowdfunding products with strategic consumers and online reviews. Electronic Commerce Research and Applications, 54, 101169.
45. Tuna, H., & Başdal, M. 2021. Curriculum evaluation of tourism undergraduate programs in Turkey: A CIPP model-based framework. Journal of Hospitality, Leisure, Sport & Tourism Education, 29, 100324.
46. Türker, N., & Süzer, Ö. 2022. Tourists' food and beverage consumption trends in the context of culinary movements: The case of Safranbolu. International Journal of Gastronomy and Food Science, 27, 100463.
47. UNEP. 2013. World's Largest Travel Site Awards Qualifying Accommodations Across the U.S. with Bronze, Silver, Gold or Platinum Status. Retrieved October from https://www.unep.org/es/node/6002
48. Verma, V. K., Chandra, B., & Kumar, S. 2019. Values and ascribed responsibility to predict consumers' attitude and concern towards green hotel visit intention. Journal of Business Research, 96, 206-216.
49. Wadud, M. A. H., Kabir, M. M., Mridha, M., Ali, M. A., Hamid, M. A., & Monowar, M. M. 2022. How can we manage offensive text in social media-a text classification approach using LSTM-BOOST. International Journal of Information Management Data Insights, 2(2), 100095.
50. Wang, Q., Zhang, W., Li, J., Mai, F., & Ma, Z. 2022. Effect of online review sentiment on product sales: The moderating role of review credibility perception. Computers in Human Behavior, 133, 107272.
51. Wei, X., & Taecharungroj, V. 2022. How to improve learning experience in MOOCs an analysis of online reviews of business courses on Coursera. The International Journal of Management Education, 20(3), 100675.
52. Williams, T., & Betak, J. 2018. A Comparison of LSA and LDA for the Analysis of Railroad Accident Text. Procedia Computer Science, 130, 98-102.
53. Wu, H., Zhang, Z., Li, X., Shang, K., Han, Y., Geng, Z., & Pan, T. 2022. A novel pedal musculoskeletal response based on differential spatio-temporal LSTM for human activity recognition. Knowledge-Based Systems, 110187.
54. Wu, L., & Noels, L. 2022. Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step. Computer Methods in Applied Mechanics and Engineering, 390, 114476.
55. Xianghua, F., Guo, L., Yanyan, G., & Zhiqiang, W. 2013. Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems, 37, 186-195.
56. Yeşiltaş, M., Gürlek, M., & Kenar, G. 2022. Organizational green culture and green employee behavior: Differences between green and non-green hotels. Journal of Cleaner Production, 343, 131051.
57. Yu, M., Cheng, M., Yang, L., & Yu, Z. 2022. Hotel guest satisfaction during COVID-19 outbreak: The moderating role of crisis response strategy. Tourism Management, 93, 104618.
58. Zamparini, L., Domènech, A., Miravet, D., & Gutiérrez, A. 2022. Green mobility at home, green mobility at tourism destinations: A cross-country study of transport modal choices of educated young adults. Journal of Transport Geography, 103, 103412.
59. Zhang, C., Peng, K., Dong, J., & Miao, L. 2022. A comprehensive operating performance assessment framework based on distributed Siamese gated recurrent unit for hot strip mill process. Applied Soft Computing, 109889.
60. Zhang, E., Li, H., Huang, Y., Hong, S., Zhao, L., & Ji, C. 2022. Practical multi-party private collaborative k-means clustering. Neurocomputing, 467, 256-265.
61. Zhang, N., Liu, R., Zhang, X.-Y., & Pang, Z.-L. 2021. The impact of consumer perceived value on repeat purchase intention based on online reviews: by the method of text mining. Data Science and Management, 3, 22-32.
62. Zhao, R., Wang, D., Yan, R., Mao, K., Shen, F., & Wang, J. 2017. Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Transactions on Industrial Electronics, 65(2), 1539-1548.
63. Zibarzani, M., Abumalloh, R. A., Nilashi, M., Samad, S., Alghamdi, O., Nayer, F. K., Ismail, M. Y., Mohd, S., & Akib, N. A. M. 2022. Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology. Technology in Society, 70, 101977.