• Abstract
• Contents
• Tables
• Figures
• Abbreviations and Symbols
• 1. Introduction
  • 1.1 Research background
  • 1.2 Research gaps
  • 1.3 Research contributions
• 2. Literature review
  • 2.1 Online reviews in marketing and hospitality and tourism literature
  • 2.2 Online reviews in restaurant literature
  • 2.3 Online reviews in green restaurants research
    • Table 2.1 Most related research on green restaurants' customer satisfaction
  • 2.4 Topic modeling methods
    • Equation (1-1): ,-.=,(,=--,-..)-,=--,-...
    • Figure 2.1 LDA generative process (Vayansky & Kumar, 2020)
    • Table 2.2 Advantages and disadvantages of LDA (Egger & Yu, 2022)
    • Table 2.3 Advantages and disadvantages of BERTopic (Egger & Yu, 2022)
  • 2.5 Aspect-based sentiment analysis
    • Figure 2.2 Approach of sentiment analysis (Wankhade et al., 2022)
• 3. Methodology
  • 3.1 Data gathering
    • Table 3.1 An example of the scraped dataset
  • 3.2 Data cleaning
  • 3.3 Research flowchart and methodologies
    • Figure 3.1 Flowchart of Studies
    • 3.3.1 Study 1 Methodology
    • 3.3.2 Labeling and creating full dataset procedure
    • Figure 3.2 The pretuned model output example
    • Figure 3.3 The complete dataset
    • 3.3.3 Study 2 Methodology
    • Figure 3.4 Base Bert (Khalid et al., 2021)
    • Figure 3.5 CNN for text classification (Nguyen et al., 2019)
    • Figure 3.6 General architecture of dense layers
    • Equation (3-1): ⁡()=,,-.−-,-.+.
    • Equation (3-2): ⁡()=,-,-−.+.
    • Equation (3-3): ⁡()=(,)
    • Figure 3.7 Proposed model
    • Figure 3.8 Distribution of aspect categories
    • Figure 3.9 Focal loss function (Lin, 2017)
    • Equation (3-4): =,-.,--(,-.=,,.-.).
    • Equation (3-5): =, - + .
    • Equation (3-6): =, - + .
    • Equation (3-7): = , ∗ - + .
    • 3.3.4 Study 3 Methodology
    • Equation (3-8): , -. = ,-.+,-.,-.+,-.,-.+, -.,-.+,-.,-.+,-.,-.+,-. ,-.
    • Table 3.2 Summary of past research on restaurants' customers' satisfaction
• 4. Studies' results
  • 4.1 Study 1: Identifying green restaurants' aspects from online reviews
    • Figure 4.1 Online reviews wordcloud
    • Table 4.1 Extracted topics via BERTopic
  • 4.2 Study 2: Aspect-based sentiment analysis
    • Table 4.2 System accuracy for joint task
    • Table 4.3 Metrics for ACD task
  • 4.3 Study 3: Investigating aspects affecting green restaurants' customers' satisfaction
    • Figure 4.2 Distribution of sentiments and rating in reviews containing all the aspects
    • Table 4.4 Correlation matrix for the first setting
    • Table 4.5 Result of first setting regression
    • Table 4.6 Correlation matrix for the second setting
    • Table 4.7 Result of second setting regression
• 5. Discussion and implications
• 6. Limitations and future research
• 7. References
  • Abdou, A. H., Hassan, T. H., Salem, A., Albakhit, A., AlMakhayitah, M., & Salama, W. (2022). The Nexus between Environmentally Sustainable Practices, Green Satisfaction, and Customer Citizenship Behavior in Eco-Friendly Hotels: Social Exchange Theory ...
  • Agrawal, S. R. (2020). Adoption of WhatsApp for Strengthening Internal CRM through Social Network Analysis. Journal of Relationship Marketing, 20(4), 261–281. https://doi.org/10.1080/15332667.2020.1802643
  • Akenji, L. (2014). Consumer scapegoatism and limits to green consumerism. Journal of Cleaner Production, 63, 13–23. https://doi.org/10.1016/j.jclepro.2013.05.022
  • Akhtar, R., Asheq, A. A., Ahmed, E., Chowdhury, U., Sufi, T., & Mostofa, G. (2022). The intricate relationships of consumers’ loyalty and their perceptions of service quality, price and satisfaction in restaurant service. The Tqm Journal, 35(2), 519–5...
  • Aktaş-Polat, S., & Polat, S. (2021). Discovery of factors affecting tourists’ fine dining experiences at five-star hotel restaurants in Istanbul. British Food Journal, 124(1), 221–238. https://doi.org/10.1108/bfj-02-2021-0138
  • Alaei, A., Becken, S., & Stantic, B. (2019). Sentiment Analysis in Tourism: Capitalizing on Big Data. Journal of Travel Research, 58(2), 175–191. https://doi.org/10.1177/0047287517747753
  • Alagarsamy, S., Mehrolia, S., & Mathew, S. (2021). How Green Consumption Value Affects Green Consumer Behaviour: The Mediating Role of Consumer Attitudes Towards Sustainable Food Logistics Practices. Vision: The Journal of Business Perspective, 25(1),...
  • Alamoudi, E. S., & Alghamdi, N. S. (2021). Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings. Journal of Decision Systems, 30(2–3), 259–281. https://doi.org/10.1080/12460125.2020.1864106
  • Alantari, H. J., Currim, I. S., Deng, Y., & Singh, S. (2022). An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews. International Journal of Research in Marketing, 39(1), 1–19. https://doi.or...
  • Ali, F., Park, E., Kwon, J., & Chae, B. (2019). 30 years of contemporary hospitality management. International Journal of Contemporary Hospitality Management, 31(7), 2641–2665. https://doi.org/10.1108/ijchm-10-2018-0832
  • Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020). Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique: A Comparative Study. In Lecture Notes in Computer Science (pp. 317–325). Springer Science+Business Media. htt...
  • Alves, H. M. (2022, November 14). Encouraging Brand Attachment on Consumer Behaviour: Pet-Friendly Tourism Segment. https://ssrn.com/abstract=4485852
  • Anandarajan, M., Hill, C., & Nolan, T. J. (2019). Practical Text Analytics. In Advances in analytics and data science. Springer International Publishing. https://doi.org/10.1007/978-3-319-95663-3
  • Andaleeb, S. S., & Conway, C. (2006). Customer satisfaction in the restaurant industry: an examination of the transaction‐specific model. Journal of Services Marketing, 20(1), 3–11. https://doi.org/10.1108/08876040610646536
  • Anderson, J. R., & Bower, G. H. (1973). Human Associative Memory. Psychology Press.
  • Angelov, D. (2020). Top2Vec: Distributed Representations of Topics. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2008.09470
  • Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the Pricing Power of Product Features by Mining Consumer Reviews. Management Science, 57(8), 1485–1509. https://doi.org/10.1287/mnsc.1110.1370
  • Athey, S., Bayati, M., Doudchenko, N., Imbens, G. W., & Khosravi, K. (2021). Matrix Completion Methods for Causal Panel Data Models. Journal of the American Statistical Association, 116(536), 1716–1730. https://doi.org/10.1080/01621459.2021.1891924
  • Bacig, M., & Young, C. A. (2019). The halo effect created for restaurants that source food locally. Journal of Foodservice Business Research, 22(3), 209–238. https://doi.org/10.1080/15378020.2019.1592654
  • Balducci, B., & Marinova, D. (2018). Unstructured data in marketing. Journal of the Academy of Marketing Science, 46(4), 557–590. https://doi.org/10.1007/s11747-018-0581-x
  • Baloglu, S., Raab, C., & Malek, K. (2020). Organizational Motivations for Green Practices in Casual Restaurants. International Journal of Hospitality & Tourism Administration, 23(2), 269–288. https://doi.org/10.1080/15256480.2020.1746216
  • Bassig, M. (2022, June 30). The Impact of Restaurant Star Ratings on Customers. ReviewTrackers. https://www.reviewtrackers.com/blog/restaurant-star-rating/
  • Belarmino, A., Demirciftci, T., & Zhang, L. (2020). Online reviews and travel magazine awards: their influence on willingness-to-pay. Journal of Revenue and Pricing Management, 20(4), 436–445. https://doi.org/10.1057/s41272-020-00256-0
  • Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2019). Uniting the Tribes: Using Text for Marketing Insight. Journal of Marketing, 84(1), 1–25. https://doi.org/10.1177/0022242919873106
  • Berger, J., Packard, G., Boghrati, R., Hsu, M., Humphreys, A., Luangrath, A. W., Moore, S. J., Nave, G., Olivola, C. Y., & Rocklage, M. D. (2022). Marketing insights from text analysis. Marketing Letters, 33(3), 365–377. https://doi.org/10.1007/s11002...
  • Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding Satisfied and Dissatisfied Hotel Customers: Text Mining of Online Hotel Reviews. Journal of Hospitality Marketing & Management, 25(1), 1–24. https://doi.org/10.1080/19368623...
  • Bi, J., Liu, Y., Fan, Z., & Cambria, E. (2019). Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. International Journal of Production Research, 57(22), 7068–7088. https://doi.org/10.1080/002...
  • Bi, J., Liu, Y., Fan, Z., & Zhang, J. Z. (2019). Wisdom of crowds: Conducting importance-performance analysis (IPA) through online reviews. Tourism Management, 70, 460–478. https://doi.org/10.1016/j.tourman.2018.09.010
  • Bilgihan, A., Seo, S., & Choi, J. (2017). Identifying restaurant satisfiers and dissatisfiers: Suggestions from online reviews. Journal of Hospitality Marketing & Management, 27(5), 601–625. https://doi.org/10.1080/19368623.2018.1396275
  • Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge Based Systems, 226, 107134. https://doi.org/10.1016/j.knosys.2021.107134
  • Blasco-Arcas, L., Lee, H. M., Kastanakis, M. N., Alcañiz, M., & Reyes-Menendez, A. (2022). The role of consumer data in marketing: A research agenda. Journal of Business Research, 146, 436–452. https://doi.org/10.1016/j.jbusres.2022.03.054
  • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84. https://doi.org/10.1145/2133806.2133826
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. https://doi.org/10.5555/944919.944937
  • Breusch, T., & Pagan, A. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 47(5), 1287. https://doi.org/10.2307/1911963
  • Cai, H., Tu, Y., Zhou, X., Yu, J., & Xia, R. (2020). Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network. https://doi.org/10.18653/v1/2020.coling-main.72
  • Calheiros, A. C., Moro, S., & Rita, P. (2017). Sentiment Classification of Consumer-Generated Online Reviews Using Topic Modeling. Journal of Hospitality Marketing & Management, 26(7), 675–693. https://doi.org/10.1080/19368623.2017.1310075
  • Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017). Sentiment Analysis Is a Big Suitcase. IEEE Intelligent Systems, 32(6), 74–80. https://doi.org/10.1109/mis.2017.4531228
  • Cantele, S., & Cassia, F. (2020). Sustainability implementation in restaurants: A comprehensive model of drivers, barriers, and competitiveness-mediated effects on firm performance. International Journal of Hospitality Management, 87, 102510. https://...
  • Chakraborty, I., Kim, M., & Sudhir, K. (2022). Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes. Journal of Marketing Research, 59(3), 600–622. https://doi.org/10.1177/00222437211052500
  • Chan, J. Y., Bea, K. T., Leow, S. M. H., Phoong, S. W., & Cheng, W. K. (2022). State of the art: a review of sentiment analysis based on sequential transfer learning. Artificial Intelligence Review, 56(1), 749–780. https://doi.org/10.1007/s10462-022-1...
  • Chan, W., & Ho, K. (2006). Hotels’ environmental management systems (ISO 14001): creative financing strategy. International Journal of Contemporary Hospitality Management, 18(4), 302–316. https://doi.org/10.1108/09596110610665311
  • Chatterjee, S. (2019). Explaining customer ratings and recommendations by combining qualitative and quantitative user generated contents. Decision Support Systems, 119, 14–22. https://doi.org/10.1016/j.dss.2019.02.008
  • Chatterjee, S. (2020). Drivers of helpfulness of online hotel reviews: A sentiment and emotion mining approach. International Journal of Hospitality Management, 85, 102356. https://doi.org/10.1016/j.ijhm.2019.102356
  • Chatterjee, S., Goyal, D., Prakash, A., & Sharma, J. (2021). Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application. Journal of Business Research, 131, 815–825. https://doi.org/10.1016/j.jbusres.2020...
  • Chaturvedi, P., Kulshreshtha, K., Tripathi, V., & Agnihotri, D. (2022). Investigating the impact of restaurants’ sustainable practices on consumers’ satisfaction and revisit intentions: a study on leading green restaurants. Asia-pacific Journal of Bus...
  • Chebolu, S. U. S., Dernoncourt, F., Lipka, N., & Solorio, T. (2022). Survey of Aspect-based Sentiment Analysis Datasets. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2204.05232
  • Chen, Z., & Qian, T. (2020). Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis. https://doi.org/10.18653/v1/2020.acl-main.340
  • Cheng, M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. International Journal of Hospitality Management, 76, 58–70. https://doi.org/10.1016/j.ijhm.2018.04.004
  • Choi, B., & La, S. (2013). The impact of corporate social responsibility (CSR) and customer trust on the restoration of loyalty after service failure and recovery. Journal of Services Marketing, 27(3), 223–233. https://doi.org/10.1108/08876041311330717
  • Choi, G., & Parsa, H. G. (2006). Green Practices II. Journal of Foodservice Business Research, 9(4), 41–63. https://doi.org/10.1300/j369v09n04_04
  • Choi, H., Joung, H., Choi, E., & Kim, H. (2021). Understanding vegetarian customers: the effects of restaurant attributes on customer satisfaction and behavioral intentions. Journal of Foodservice Business Research, 25(3), 353–376. https://doi.org/10....
  • Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. In Routledge eBooks. https://doi.org/10.4324/9780203771587
  • Culotta, A., & Cutler, J. (2016). Mining Brand Perceptions from Twitter Social Networks. Marketing Science, 35(3), 343–362. https://doi.org/10.1287/mksc.2015.0968
  • Del Rocío Martínez-Torres, M., & Toral, S. (2019). A machine learning approach for the identification of the deceptive reviews in the hospitality sector using unique attributes and sentiment orientation. Tourism Management, 75, 393–403. https://doi.or...
  • Devlin, J. (2018, October 11). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv.org. https://arxiv.org/abs/1810.04805v2
  • Dinçer, M. Z., & Alrawadieh, Z. (2017). Negative Word of Mouse in the Hotel Industry: A Content Analysis of Online Reviews on Luxury Hotels in Jordan. Journal of Hospitality Marketing & Management, 26(8), 785–804. https://doi.org/10.1080/19368623.2017...
  • Ding, H., Xiao, Q., Jin, W., Shan, Y. G., & Yu, H. (2022). A Novel Cascade Model for End-to-End Aspect-Based Social Comment Sentiment Analysis. Electronics, 11(12), 1810. https://doi.org/10.3390/electronics11121810
  • Dipietro, R. B. (2013). A Comparative Study of Customer Perceptions Regarding Green Restaurant Practices: Fast Food vs. Upscale Casual. https://www.semanticscholar.org/paper/A-Comparative-Study-of-Customer-Perceptions-Green-Dipietro-Gregory/5081ac91cc...
  • DiPietro, R. B., Cao, Y., & Partlow, C. G. (2013). Green practices in upscale foodservice operations. International Journal of Contemporary Hospitality Management, 25(5), 779–796. https://doi.org/10.1108/ijchm-may-2012-0082
  • Doborjeh, Z. G., Hemmington, N., Doborjeh, M. G., & Yang, J. (2021). Artificial intelligence: a systematic review of methods and applications in hospitality and tourism. International Journal of Contemporary Hospitality Management, 34(3), 1154–1176. h...
  • Dozat, T. (2016, February 18). Incorporating Nesterov Momentum into Adam. OpenReview. https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ
  • Egger, R. (2022). Applied Data Science in Tourism: Interdisciplinary Approaches, Methodologies, and Applications. Springer.
  • Egger, R., Pagiri, A., Prodinger, B., Liu, R., & Wettinger, F. (2022). Topic Modelling of Tourist Dining Experiences Based on the GLOBE Model. In Springer eBooks (pp. 356–368). https://doi.org/10.1007/978-3-030-94751-4_32
  • Egger, R., & Yu, J. L. (2021). Identifying hidden semantic structures in Instagram data: a topic modelling comparison. Tourism Review. https://doi.org/10.1108/tr-05-2021-0244
  • Egger, R., & Yu, J. L. (2022). A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Frontiers in Sociology, 7. https://doi.org/10.3389/fsoc.2022.886498
  • Emekci, S. (2019). Green consumption behaviours of consumers within the scope of TPB. Journal of Consumer Marketing, 36(3), 410–417. https://doi.org/10.1108/jcm-05-2018-2694
  • Engler, T., Winter, P. M., & Schulz, M. (2015). Understanding online product ratings: A customer satisfaction model. Journal of Retailing and Consumer Services, 27, 113–120. https://doi.org/10.1016/j.jretconser.2015.07.010
  • Eren, R., Uslu, A., & Aydın, A. (2023). The Effect of Service Quality of Green Restaurants on Green Restaurant Image and Revisit Intention: The Case of Istanbul. Sustainability, 15(7), 5798. https://doi.org/10.3390/su15075798
  • Falk, R. F., & Miller, N. H. (1992). A Primer for Soft Modeling. https://psycnet.apa.org/record/1992-98610-000
  • Farouk, S., & Guzmán, F. (2021). Negative online reviews, brand equity and emotional contagion. European Journal of Marketing, 55(11), 2825–2870. https://doi.org/10.1108/ejm-10-2019-0820
  • Filimonau, V., Todorova, E. K., Mzembe, A. N., Sauer, L., & Yankholmes, A. (2020). A comparative study of food waste management in full service restaurants of the United Kingdom and the Netherlands. Journal of Cleaner Production, 258, 120775. https://...
  • Gan, Q., Ferns, B. H., Yu, Y., & Jin, L. (2016). A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews. Journal of Quality Assurance in Hospitality & Tourism, 18(4), 465–492. https://doi.org/10.1080/1528008x.2016.1250243
  • Gao, S., Tang, O., Wang, H., & Yin, P. (2018). Identifying competitors through comparative relation mining of online reviews in the restaurant industry. International Journal of Hospitality Management, 71, 19–32. https://doi.org/10.1016/j.ijhm.2017.09...
  • Gao, Y., & Mattila, A. S. (2014). Improving consumer satisfaction in green hotels: The roles of perceived warmth, perceived competence, and CSR motive. International Journal of Hospitality Management, 42, 20–31. https://doi.org/10.1016/j.ijhm.2014.06.003
  • Geerts, W. (2014). Environmental certification schemes: Hotel managers’ views and perceptions. International Journal of Hospitality Management, 39, 87–96. https://doi.org/10.1016/j.ijhm.2014.02.007
  • Geetha, M., Singha, P., & Sinha, S. P. (2017). Relationship between customer sentiment and online customer ratings for hotels - An empirical analysis. Tourism Management, 61, 43–54. https://doi.org/10.1016/j.tourman.2016.12.022
  • Ginsberg, J. M. (2004, October 15). Choosing the Right Green-Marketing Strategy. MIT Sloan Management Review. https://sloanreview.mit.edu/article/choosing-the-right-greenmarketing-strategy/
  • Green Restaurant Association Certification Standards. (n.d.). Green Restaurant. https://www.dinegreen.com/certification-standards
  • Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2203.05794
  • Gupta, S., McLaughlin, E. W., & Gómez, M. I. (2019). 2. Guest Satisfaction and Restaurant Performance. In Cornell University Press eBooks (pp. 33–53). https://doi.org/10.7591/9781501736520-005
  • Gwin, C. H., & Gwin, C. R. (2003). Product Attributes Model: A Tool for Evaluating Brand Positioning. The Journal of Marketing Theory and Practice, 11(2), 30–42. https://doi.org/10.1080/10696679.2003.11658494
  • Ha, H., DO, Prasad, P. W. C., Maag, A., & Alsadoon, A. (2019). Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review. Expert Systems With Applications, 118, 272–299. https://doi.org/10.1016/j.eswa.2018.10.003
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/mtp1069-6679190202
  • Haller, Karl, Jim Lee, Cheung, Jane (2020). Meet the 2020 consumers driving change”, IBM research insight 2020 report., available at https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/consumer-2020
  • Han, H. (2020). Theory of green purchase behavior (TGPB): A new theory for sustainable consumption of green hotel and green restaurant products. Business Strategy and the Environment, 29(6), 2815–2828. https://doi.org/10.1002/bse.2545
  • Han, H. (2021). Consumer behavior and environmental sustainability in tourism and hospitality: a review of theories, concepts, and latest research. Journal of Sustainable Tourism, 29(7), 1021–1042. https://doi.org/10.1080/09669582.2021.1903019
  • Han, H., & Hyun, S. S. (2017). Impact of hotel-restaurant image and quality of physical-environment, service, and food on satisfaction and intention. International Journal of Hospitality Management, 63, 82–92. https://doi.org/10.1016/j.ijhm.2017.03.006
  • Hannigan, T. R., Haans, R. F. J., Vakili, K., Tchalian, H., Glaser, V., Wang, M. S., Kaplan, S., & Jennings, P. D. (2019). Topic Modeling in Management Research: Rendering New Theory from Textual Data. The Academy of Management Annals, 13(2), 586–632....
  • Harbert, T. (2021). Tapping the power of unstructured data. https://mitsloan.mit.edu/ideas-made-to-matter/tapping-power-unstructured-data
  • Hartmann, J., Huppertz, J., Schamp, C., & Heitmann, M. (2019). Comparing automated text classification methods. International Journal of Research in Marketing, 36(1), 20–38. https://doi.org/10.1016/j.ijresmar.2018.09.009
  • Heng, Y., Gao, Z., Jiang, Y., & Chen, X. (2018). Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42, 161–168. https://doi.org/10.1016/j.jretconser.2018.02.006
  • Higgins-Desbiolles, F., Moskwa, E., & Wijesinghe, G. (2019). How sustainable is sustainable hospitality research? A review of sustainable restaurant literature from 1991 to 2015. Current Issues in Tourism, 22(13), 1551–1580. https://doi.org/10.1080/13...
  • Hinton, G. E. (2012, July 3). Improving neural networks by preventing co-adaptation of feature detectors. arXiv.org. https://arxiv.org/abs/1207.0580v1
  • Hong, S., & Pittman, M. (2020). eWOM anatomy of online product reviews: interaction effects of review number, valence, and star ratings on perceived credibility. International Journal of Advertising, 39(7), 892–920. https://doi.org/10.1080/02650487.20...
  • Hossain, E., Sharif, O., Hoque, M. M., & Sarker, I. H. (2021). SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews. In Advances in intelligent systems and computing (pp. 193–203). Springer Nature. https://doi.org/10.1007/9...
  • Hossain, M. S., & Rahman, M. F. (2022). Detection of potential customers’ empathy behavior towards customers’ reviews. Journal of Retailing and Consumer Services, 65, 102881. https://doi.org/10.1016/j.jretconser.2021.102881
  • Hu, H., Parsa, H. G., & Self, J. A. (2010). The Dynamics of Green Restaurant Patronage. Cornell Hospitality Quarterly, 51(3), 344–362. https://doi.org/10.1177/1938965510370564
  • Hu, N., Koh, N. S., & Reddy, S. K. (2014). Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, 57, 42–53. https://doi.org/10.1016/j.dss.2013.07.009
  • Hu, N., Zhang, T., Gao, B., & Bose, I. (2019). What do hotel customers complain about? Text analysis using structural topic model. Tourism Management, 72, 417–426. https://doi.org/10.1016/j.tourman.2019.01.002
  • Hu, Z., Wang, Z., Wang, Y., & Tan, A. (2023). MSRL-Net: A multi-level semantic relation-enhanced learning network for aspect-based sentiment analysis. Expert Systems With Applications, 217, 119492. https://doi.org/10.1016/j.eswa.2022.119492
  • Huang, M., & Rust, R. T. (2020). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
  • Hwang, K., & Lee, B. (2019). Pride, mindfulness, public self-awareness, affective satisfaction, and customer citizenship behaviour among green restaurant customers. International Journal of Hospitality Management, 83, 169–179. https://doi.org/10.1016/...
  • Iglesias, M., & Guillén, M. D. (2004). Perceived quality and price: their impact on the satisfaction of restaurant customers. International Journal of Contemporary Hospitality Management, 16(6), 373–379. https://doi.org/10.1108/09596110410550824
  • Im, J., Song, T., Lee, Y., & Kim, J. (2021). Confirmatory aspect-level opinion mining processes for tourism and hospitality research: a proposal of DiSSBUS. Current Issues in Tourism, 25(12), 1876–1894. https://doi.org/10.1080/13683500.2021.1974357
  • Jabbour, D. (2023). 3-Star Reviews Result In A -70% Decrease In Trust [Data Study]. Go Fish Digital. https://gofishdigital.com/blog/3-star-reviews-result-in-70-decrease-in-trust-data-study/
  • Jain, P. K., Pamula, R., & Srivastava, G. (2021). A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Computer Science Review, 41, 100413. https://doi.org/10.1016/j.cosrev.2021.100413
  • Jang, Y. H., Kim, W. G., & Bonn, M. A. (2011). Generation Y consumers’ selection attributes and behavioral intentions concerning green restaurants. International Journal of Hospitality Management, 30(4), 803–811. https://doi.org/10.1016/j.ijhm.2010.12...
  • Jeong, E., & Jang, S. (2011). Restaurant experiences triggering positive electronic word-of-mouth (eWOM) motivations. International Journal of Hospitality Management, 30(2), 356–366. https://doi.org/10.1016/j.ijhm.2010.08.005
  • Jeong, E., Jang, S., Day, J., & Ha, S. (2014). The impact of eco-friendly practices on green image and customer attitudes: An investigation in a café setting. International Journal of Hospitality Management, 41, 10–20. https://doi.org/10.1016/j.ijhm.2...
  • Jia, S. S. (2018). Behind the ratings: Text mining of restaurant customers’ online reviews. International Journal of Market Research, 60(6), 561–572. https://doi.org/10.1177/1470785317752048
  • Jia, S. S. (2019). Measuring tourists’ meal experience by mining online user generated content about restaurants. Scandinavian Journal of Hospitality and Tourism, 19(4–5), 371–389. https://doi.org/10.1080/15022250.2019.1651671
  • Jia, S. S. (2020). Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews. Tourism Management, 78, 104071. https://doi.org/10.1016/j.tourman.2019.104071
  • Jiménez, F., & Mendoza, N. A. (2013). Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products. Journal of Interactive Marketing, 27(3), 226–235. https://doi.org/10.1016/j.intmar.2013.04.004
  • Kamila, S. (2022, November 7). AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis. arXiv.org. https://arxiv.org/abs/2211.03837v1
  • Kasim, A., & Ismail, A. (2012). Environmentally friendly practices among restaurants: drivers and barriers to change. Journal of Sustainable Tourism, 20(4), 551–570. https://doi.org/10.1080/09669582.2011.621540
  • Kassinis, G. I., & Soteriou, A. C. (2009). GREENING THE SERVICE PROFIT CHAIN: THE IMPACT OF ENVIRONMENTAL MANAGEMENT PRACTICES. Production and Operations Management, 12(3), 386–403. https://doi.org/10.1111/j.1937-5956.2003.tb00210.x
  • Khalid, U., Beg, M., & Arshad, M. U. (2021). RUBERT: A Bilingual Roman Urdu BERT Using Cross Lingual Transfer Learning. ResearchGate. https://www.researchgate.net/publication/349546860_RUBERT_A_Bilingual_Roman_Urdu_BERT_Using_Cross_Lingual_Transfer_Le...
  • Khine, W. S. S., & Aung, N. T. T. (2019). Applying Deep Learning Approach to Targeted Aspect-based Sentiment Analysis for Restaurant Domain. https://doi.org/10.1109/aitc.2019.8920880
  • Kim, H., & Bachman, J. R. (2019). Examining customer perceptions of restaurant restroom cleanliness and their impact on satisfaction and intent to return. Journal of Foodservice Business Research, 22(2), 191–208. https://doi.org/10.1080/15378020.2019....
  • Kim, H., & So, K. K. F. (2022). Two decades of customer experience research in hospitality and tourism: A bibliometric analysis and thematic content analysis. International Journal of Hospitality Management, 100, 103082. https://doi.org/10.1016/j.ijhm...
  • Kim, J., & Im, J. (2018). Proposing a missing data method for hospitality research on online customer reviews. International Journal of Contemporary Hospitality Management, 30(11), 3250–3267. https://doi.org/10.1108/ijchm-10-2017-0708
  • Kim, M. K., & Hall, C. M. (2020). Can sustainable restaurant practices enhance customer loyalty? The roles of value theory and environmental concerns. Journal of Hospitality and Tourism Management, 43, 127–138. https://doi.org/10.1016/j.jhtm.2020.03.004
  • Kim, S. Y., Lee, K., & Fairhurst, A. (2017). The review of “green” research in hospitality, 2000-2014. International Journal of Contemporary Hospitality Management, 29(1), 226–247. https://doi.org/10.1108/ijchm-11-2014-0562
  • Kim, T. T., Almanza, B., Ma, J., Park, H., & Kline, S. F. (2021). The cleanliness of restaurants: ATP tests (reality) vs consumers’ perception. International Journal of Contemporary Hospitality Management, 33(3), 893–911. https://doi.org/10.1108/ijchm...
  • Kim, W., & Chae, B. (2018). Understanding the relationship among resources, social media use and hotel performance. International Journal of Contemporary Hospitality Management, 30(9), 2888–2907. https://doi.org/10.1108/ijchm-02-2017-0085
  • Kirilenko, A. P., Stepchenkova, S., & Dai, X. (2021). Automated topic modeling of tourist reviews: Does the Anna Karenina principle apply? Tourism Management, 83, 104241. https://doi.org/10.1016/j.tourman.2020.104241
  • Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 205395171452848. https://doi.org/10.1177/2053951714528481
  • Konuk, F. A. (2019). The influence of perceived food quality, price fairness, perceived value and satisfaction on customers’ revisit and word-of-mouth intentions towards organic food restaurants. Journal of Retailing and Consumer Services, 50, 103–110...
  • Korfiatis, N., Stamolampros, P., Kourouthanassis, P. E., & Sagiadinos, V. (2019). Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews. Expert Systems With Applications, 116, 472–486. htt...
  • Kumar, V., & Srivastava, A. (2022). Trends in the thematic landscape of corporate social responsibility research: A structural topic modeling approach. Journal of Business Research, 150, 26–37. https://doi.org/10.1016/j.jbusres.2022.05.075
  • Kumari, R., Verma, R. K., Debata, B. R., & Ting, H. (2022). A systematic literature review on the enablers of green marketing adoption: Consumer perspective. Journal of Cleaner Production, 366, 132852. https://doi.org/10.1016/j.jclepro.2022.132852
  • Kwok, L., & Huang, Y. (2019). Green attributes of restaurants: Do consumers, owners, and managers think alike? International Journal of Hospitality Management, 83, 28–32. https://doi.org/10.1016/j.ijhm.2019.03.011
  • Kwok, L., Huang, Y., & Hu, L. (2016). Green attributes of restaurants: What really matters to consumers? International Journal of Hospitality Management, 55, 107–117. https://doi.org/10.1016/j.ijhm.2016.03.002
  • Kwon, W., Lee, M., & Back, K. (2020). Exploring the underlying factors of customer value in restaurants: A machine learning approach. International Journal of Hospitality Management, 91, 102643. https://doi.org/10.1016/j.ijhm.2020.102643
  • Kwon, W., Lee, M., & Bowen, J. T. (2021). Exploring Customers’ Luxury Consumption in Restaurants: A Combined Method of Topic Modeling and Three-Factor Theory. Cornell Hospitality Quarterly, 63(1), 66–77. https://doi.org/10.1177/19389655211037667
  • LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  • Lee, M., Kwon, W., & Back, K. (2021). Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making. International Journal of Contemporary Hospitality Management...
  • Lee, M., Lee, S., & Koh, Y. W. (2019). Multisensory experience for enhancing hotel guest experience. International Journal of Contemporary Hospitality Management, 31(11), 4313–4337. https://doi.org/10.1108/ijchm-03-2018-0263
  • Li, G., Li, G., & Law, R. (2019). Discovering implicit activity preferences in travel itineraries by topic modeling. Tourism Management, 75, 435–446. https://doi.org/10.1016/j.tourman.2019.06.011
  • Li, H., Liu, H., & Zhang, Z. (2020). Online persuasion of review emotional intensity: A text mining analysis of restaurant reviews. International Journal of Hospitality Management, 89, 102558. https://doi.org/10.1016/j.ijhm.2020.102558
  • Li, H., & Ma, L. (2020). Charting the Path to Purchase Using Topic Models. Journal of Marketing Research, 57(6), 1019–1036. https://doi.org/10.1177/0022243720954376
  • Li, H., Yu, B. X., Li, G., & Huicai, G. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 104707. https://doi.org/10.1016/j.tourman.2022.104707
  • Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, 68, 301–323. https://doi.org/10.1016/j.tourman.2018.03.009
  • Li, Q., Li, S., Zhang, S., Hu, J., & Hu, J. (2019). A Review of Text Corpus-Based Tourism Big Data Mining. Applied Sciences, 9(16), 3300. https://doi.org/10.3390/app9163300
  • Li, X., Bing, L., Zhang, W., & Lam, W. (2019). Exploiting BERT for End-to-End Aspect-based Sentiment Analysis. https://doi.org/10.18653/v1/d19-5505
  • Lin, T. (2017, August 7). Focal Loss for Dense Object Detection. arXiv.org. https://arxiv.org/abs/1708.02002v2
  • Lin, T., Sun, A., & Wang, Y. (2022). EDU-Capsule: aspect-based sentiment analysis at clause level. Knowledge and Information Systems, 65(2), 517–541. https://doi.org/10.1007/s10115-022-01797-z
  • Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458–468. https://doi.org/10.1016/j.tourman.2007.05.011
  • Litvin, S. W., Goldsmith, R. E., & Pan, B. (2018). A retrospective view of electronic word-of-mouth in hospitality and tourism management. International Journal of Contemporary Hospitality Management, 30(1), 313–325. https://doi.org/10.1108/ijchm-08-2...
  • Liu, B. (2020). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press.
  • Liu, J. (2022, February 17). Fine-Grained Sentiment Analysis: Recent Progress. https://publications.waset.org/10012408/fine-grained-sentiment-analysis-recent-progress
  • Liu, J., Teng, Z., Cui, L., Liu, H., & Zhang, Y. (2021). Solving Aspect Category Sentiment Analysis as a Text Generation Task. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2021....
  • Liu, J., Yu, Y., Mehraliyev, F., Hu, S., & Chen, J. (2022). What affects the online ratings of restaurant consumers: a research perspective on text-mining big data analysis. International Journal of Contemporary Hospitality Management, 34(10), 3607–36...
  • Liu, P., & Tse, E. C. (2018). Exploring factors on customers’ restaurant choice: an analysis of restaurant attributes. British Food Journal, 120(10), 2289–2303. https://doi.org/10.1108/bfj-10-2017-0561
  • Liu, X., Lee, D., & Srinivasan, K. (2019). Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning. Journal of Marketing Research, 56(6), 918–943. https://doi.org/10.1177/0022243719866690
  • Liu, Y. (2019, July 26). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv.org. https://arxiv.org/abs/1907.11692v1
  • Liu, Y., Pang, B., & Wang, X. (2019). Opinion spam detection by incorporating multimodal embedded representation into a probabilistic review graph. Neurocomputing, 366, 276–283. https://doi.org/10.1016/j.neucom.2019.08.013
  • Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151. https://doi.org/10.1016/j.tourman.2014.09.020
  • Lorenzini, B. (1994). The green restaurant, part II: Systems and service. ResearchGate. https://www.researchgate.net/publication/288948605_The_green_restaurant_part_II_Systems_and_service/references
  • Lu, W., & Stepchenkova, S. (2014). User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software. Journal of Hospitality Marketing & Management, 24(2), 119–154. https://doi.org/10.1080/19368623.2014.9...
  • Luca, M. (2016). Reviews, Reputation, and Revenue: The Case of Yelp.Com. Social Science Research Network. https://doi.org/10.2139/ssrn.1928601
  • Luo, J., Li, G., Li, G., & Law, R. (2021). Understanding service attributes of robot hotels: A sentiment analysis of customer online reviews. International Journal of Hospitality Management, 98, 103032. https://doi.org/10.1016/j.ijhm.2021.103032
  • Luo, Y., & Xu, X. (2019). Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp. Sustainability, 11(19), 5254. https://doi.org/10.3390/su11195254
  • Luo, Y., & Xu, X. (2021). Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic. International Journal of Hospitality Management, 94, 102849. https://doi.org/10.1016/j.ijhm.2020.102849
  • Luyu, D., Ji, S., Zhang, C., Zhang, Q., Chiu, D. K., Liqing, Q., & Li, D. (2018). An unsupervised topic-sentiment joint probabilistic model for detecting deceptive reviews. Expert Systems With Applications, 114, 210–223. https://doi.org/10.1016/j.eswa...
  • Lv, H., Shi, S., & Gursoy, D. (2021). A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism. Journal of Hospitality Marketing & Management, 31(2), 145–175. https://doi.org/...
  • Ma, E., Cheng, M., & Hsiao, A. (2018). Sentiment analysis – a review and agenda for future research in hospitality contexts. International Journal of Contemporary Hospitality Management, 30(11), 3287–3308. https://doi.org/10.1108/ijchm-10-2017-0704
  • Ma, Y., Xiang, Z., Du, Q., & Fan, W. (2018). Effects of user-provided photos on hotel review helpfulness: An analytical approach with deep leaning. International Journal of Hospitality Management, 71, 120–131. https://doi.org/10.1016/j.ijhm.2017.12.008
  • MacLaurin, D. J., & MacLaurin, T. (2000). Customer Perceptions of Singapore’s Theme Restaurants. Cornell Hotel and Restaurant Administration Quarterly, 41(3), 75–85. https://doi.org/10.1177/001088040004100319
  • Madanaguli, A. T., Dhir, A., Kaur, P., Srivastava, S., & Singh, G. (2022). Environmental sustainability in restaurants. A systematic review and future research agenda on restaurant adoption of green practices. Scandinavian Journal of Hospitality and T...
  • Malzer, C., & Baum, M. (2020). A Hybrid Approach To Hierarchical Density-based Cluster Selection. https://doi.org/10.1109/mfi49285.2020.9235263
  • Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis—A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16–32. https://doi.org/10.1016/j.cosrev.2017.10.002
  • Marchiori, E., & Cantoni, L. (2015). The role of prior experience in the perception of a tourism destination in user-generated content. Journal of Destination Marketing and Management, 4(3), 194–201. https://doi.org/10.1016/j.jdmm.2015.06.001
  • Mariani, M. M., & Baggio, R. (2021). Big data and analytics in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 34(1), 231–278. https://doi.org/10.1108/ijchm-03-2021-0301
  • Mariani, M. M., Borghi, M., & Okumus, F. (2020). Unravelling the effects of cultural differences in the online appraisal of hospitality and tourism services. International Journal of Hospitality Management, 90, 102606. https://doi.org/10.1016/j.ijhm.2...
  • Martin, C., Torres, J. P., Aguilar, R. M., & Diaz, S. E. (2018). Using Deep Learning to Predict Sentiments: Case Study in Tourism. Complexity, 2018, 1–9. https://doi.org/10.1155/2018/7408431
  • Mathayomchan, B., & Taecharungroj, V. (2020). “How was your meal?” Examining customer experience using Google maps reviews. International Journal of Hospitality Management, 90, 102641. https://doi.org/10.1016/j.ijhm.2020.102641
  • Mathwick, C., & Mosteller, J. (2017). Online Reviewer Engagement. Journal of Service Research, 20(2), 204–218. https://doi.org/10.1177/1094670516682088
  • Mattila, A. S. (2001). Emotional bonding and restaurant loyalty. Cornell Hotel and Restaurant Administration Quarterly, 42(6), 73–79. https://doi.org/10.1016/s0010-8804(01)81012-0
  • McInnes, L. (2018, February 9). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv.org. https://arxiv.org/abs/1802.03426
  • Meek, S., Wilk, V., & Lambert, C. (2021). A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews. Journal of Business Research, 125, 354–367. https://doi.org/10.1016/j.jbusres.2020.12.001
  • Mejia, J., Mankad, S., & Gopal, A. (2021). Service Quality Using Text Mining: Measurement and Consequences. Manufacturing & Service Operations Management, 23(6), 1354–1372. https://doi.org/10.1287/msom.2020.0883
  • Mishra, R. K., Urolagin, S., Jothi, J. a. A., Neogi, A. S., & Nawaz, N. (2021). Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic. Frontiers in Computer Science, 3. https://doi.org/10.3389/fcomp.2021.775368
  • Mittal, D., & Agrawal, S. R. (2022). Determining banking service attributes from online reviews: text mining and sentiment analysis. International Journal of Bank Marketing, 40(3), 558–577. https://doi.org/10.1108/ijbm-08-2021-0380
  • Moon, S. H. (2021). Investigating beliefs, attitudes, and intentions regarding green restaurant patronage: An application of the extended theory of planned behavior with moderating effects of gender and age. International Journal of Hospitality Manage...
  • Mu, W., Spaargaren, G., & Lansink, A. O. (2019). Mobile Apps for Green Food Practices and the Role for Consumers: A Case Study on Dining Out Practices with Chinese and Dutch Young Consumers. Sustainability, 11(5), 1275. https://doi.org/10.3390/su11051275
  • Murtadha, A. H., Pan, S., Wen, B., Su, J., Zhang, W., & Liu, Y. (2022). BERT-ASC: Implicit Aspect Representation Learning through Auxiliary-Sentence Construction for Sentiment Analysis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2203...
  • Namkung, Y., & Jang, D. S. (2008). Are highly satisfied restaurant customers really different? A quality perception perspective. International Journal of Contemporary Hospitality Management, 20(2), 142–155. https://doi.org/10.1108/09596110810852131
  • Namkung, Y., & Jang, S. (2013). Effects of restaurant green practices on brand equity formation: Do green practices really matter? International Journal of Hospitality Management, 33, 85–95. https://doi.org/10.1016/j.ijhm.2012.06.006
  • Namkung, Y., & Jang, S. (2016). Are Consumers Willing to Pay more for Green Practices at Restaurants? Journal of Hospitality & Tourism Research, 41(3), 329–356. https://doi.org/10.1177/1096348014525632
  • Nandini, A. S., & Kumar, R. (2019). Green Kitchen Family Restaurant: Managing the New Age Customer. South Asian Journal of Business and Management Cases, 8(2), 155–166. https://doi.org/10.1177/2277977919833768
  • Neff, R. A., Spiker, M. L., & Truant, P. T. (2015). Wasted Food: U.S. Consumers’ Reported Awareness, Attitudes, and Behaviors. PLOS ONE, 10(6), e0127881. https://doi.org/10.1371/journal.pone.0127881
  • Nicolau, J. L., Guix, M., Hernandez-Maskivker, G., & Molenkamp, N. B. (2020). Millennials’ willingness to pay for green restaurants. International Journal of Hospitality Management, 90, 102601. https://doi.org/10.1016/j.ijhm.2020.102601
  • Nguyen, V. T., Anh, T. H., & Yang, H. (2019). Real-time event detection using recurrent neural network in social sensors. International Journal of Distributed Sensor Networks, 15(6), 155014771985649. https://doi.org/10.1177/1550147719856492
  • Nilashi, M., Ahmadi, H., Arji, G., َAlsalem, K. O., Samad, S., Ghabban, F., Alzahrani, A. I., Ahani, A., & Alarood, A. A. (2021). Big social data and customer decision making in vegetarian restaurants: A combined machine learning method. Journal of Re...
  • Oh, M., & Kim, S. (2021). Role of Emotions in Fine Dining Restaurant Online Reviews: The Applications of Semantic Network Analysis and a Machine Learning Algorithm. International Journal of Hospitality & Tourism Administration, 23(5), 875–903. https:/...
  • Osgood, C. E., & Tannenbaum, P. H. (1955). The principle of congruity in the prediction of attitude change. Psychological Review, 62(1), 42–55. https://doi.org/10.1037/h0048153
  • O’Shea, K., & Nash, R. R. (2015). An Introduction to Convolutional Neural Networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1511.08458
  • Özel, Ç. H. (2015). Marketing to Children in Tourism Industry: Descriptive Analysis of Kid-Friendly Hotels’ Practices in Turkey. In Advances in culture, tourism and hospitality research. Emerald Publishing Limited. https://doi.org/10.1108/s1871-317320...
  • Padma, P., & Ahn, J. (2020). Guest satisfaction & dissatisfaction in luxury hotels: An application of big data. International Journal of Hospitality Management, 84, 102318. https://doi.org/10.1016/j.ijhm.2019.102318
  • Pantelidis, I. (2010). Electronic Meal Experience: A Content Analysis of Online Restaurant Comments. Cornell Hospitality Quarterly, 51(4), 483–491. https://doi.org/10.1177/1938965510378574
  • Papers with Code - Aspect-Based Sentiment Analysis (ABSA). (n.d.). https://paperswithcode.com/task/aspect-based-sentiment-analysis
  • Park, E., Chae, B., & Kwon, J. (2018). The structural topic model for online review analysis. Journal of Hospitality and Tourism Technology, 11(1), 1–17. https://doi.org/10.1108/jhtt-08-2017-0075
  • Park, E., Chae, B., Kwon, J., & Kim, W. (2020). The Effects of Green Restaurant Attributes on Customer Satisfaction Using the Structural Topic Model on Online Customer Reviews. Sustainability, 12(7), 2843. https://doi.org/10.3390/su12072843
  • Park, E., Kim, W., & Kwon, J. (2020). Understanding the relationship between green restaurant certification programs and a green restaurant image: the case of TripAdvisor reviews. Kybernetes, 50(6), 1689–1703. https://doi.org/10.1108/k-02-2020-0112
  • Park, E., Kwon, J., Chae, B., & Kim, S. W. (2021). What Are the Salient and Memorable Green-Restaurant Attributes? Capturing Customer Perceptions From User-Generated Content. SAGE Open, 11(3), 215824402110315. https://doi.org/10.1177/21582440211031546
  • Park, S., Ok, C. “., & Chae, B. (2016). Using Twitter Data for Cruise Tourism Marketing and Research. Journal of Travel & Tourism Marketing, 33(6), 885–898. https://doi.org/10.1080/10548408.2015.1071688
  • Parsa, H. G., Gregory, A. M., Self, J. A., & Dutta, K. (2017). Consumer Behaviour in Restaurants: Assessing the Importance of Restaurant Attributes in Consumer Patronage and Willingness to Pay. In Hospitality Marketing and Consumer Behavior. https://d...
  • Pashchenko, Y., Rahman, M. F., Hossain, M. S., Uddin, K., & Islam, T. (2022). Emotional and the normative aspects of customers’ reviews. Journal of Retailing and Consumer Services, 68, 103011. https://doi.org/10.1016/j.jretconser.2022.103011
  • Peiró-Signes, A., Segarra-Oña, M., Verma, R., Mondéjar-Jiménez, J., & Vargas-Vargas, M. (2014). The Impact of Environmental Certification on Hotel Guest Ratings. Cornell Hospitality Quarterly, 55(1), 40–51. https://doi.org/10.1177/1938965513503488
  • Peng, H., Ma, Y., Li, Y., & Cambria, E. (2018). Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowledge Based Systems, 148, 167–176. https://doi.org/10.1016/j.knosys.2018.02.034
  • Peng, H., Zhang, H., & Wang, J. (2018). Cloud decision support model for selecting hotels on TripAdvisor.com with probabilistic linguistic information. International Journal of Hospitality Management, 68, 124–138. https://doi.org/10.1016/j.ijhm.2017.1...
  • Philander, K. S., & Zhong, Y. (2016). Twitter sentiment analysis: Capturing sentiment from integrated resort tweets. International Journal of Hospitality Management, 55, 16–24. https://doi.org/10.1016/j.ijhm.2016.02.001
  • Plank, A. (2016). The hidden risk in user-generated content: An investigation of ski tourers’ revealed risk-taking behavior on an online outdoor sports platform. Tourism Management, 55, 289–296. https://doi.org/10.1016/j.tourman.2016.02.013
  • Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Al-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N. V., Kotelnikov, E. V., Bel, N., Jiménez-Zafra, S. ...
  • Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., & Manandhar, S. (2014). SemEval-2014 Task 4: Aspect Based Sentiment Analysis. https://doi.org/10.3115/v1/s14-2004
  • Prayag, G., Hassibi, S., & Nunkoo, R. (2018). A systematic review of consumer satisfaction studies in hospitality journals: conceptual development, research approaches and future prospects. Journal of Hospitality Marketing & Management, 28(1), 51–80. ...
  • Puranam, D., Narayan, V., & Kadiyali, V. (2017). The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors. Marketing Science, 36(5), 726–746. https://doi.org/10.1287/mksc.2017.1048
  • Rafay, A., Suleman, M., & Alim, A. (2020). Robust Review Rating Prediction Model based on Machine and Deep Learning: Yelp Dataset. https://doi.org/10.1109/icetst49965.2020.9080713
  • Rahmadian, E., Feitosa, D., & Zwitter, A. (2021). A systematic literature review on the use of big data for sustainable tourism. Current Issues in Tourism, 25(11), 1711–1730. https://doi.org/10.1080/13683500.2021.1974358
  • Rajaguru, R., & Hassanli, N. (2018). The role of trip purpose and hotel star rating on guests’ satisfaction and WOM. International Journal of Contemporary Hospitality Management, 30(5), 2268–2286. https://doi.org/10.1108/ijchm-01-2017-0044
  • Rajaram, P., & Sudhir, K. (2020). Video Influencers: Unboxing the Mystique. Social Science Research Network. https://doi.org/10.2139/ssrn.3752107
  • Ray, A., Bala, P. K., & Jain, R. (2020). Utilizing emotion scores for improving classifier performance for predicting customer’s intended ratings from social media posts. Benchmarking: An International Journal, 28(2), 438–464. https://doi.org/10.1108/...
  • Ray, A., Bala, P. K., & Rana, N. P. (2021). Exploring the drivers of customers’ brand attitudes of online travel agency services: A text-mining based approach. Journal of Business Research, 128, 391–404. https://doi.org/10.1016/j.jbusres.2021.02.028
  • Reisenbichler, M., & Reutterer, T. (2018). Topic modeling in marketing: recent advances and research opportunities. Journal of Business Economics, 89(3), 327–356. https://doi.org/10.1007/s11573-018-0915-7
  • Rocklage, M. D., Rucker, D. D., & Nordgren, L. F. (2021). Mass-scale emotionality reveals human behaviour and marketplace success. Nature Human Behaviour, 5(10), 1323–1329. https://doi.org/10.1038/s41562-021-01098-5
  • Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the Space of Topic Coherence Measures. https://doi.org/10.1145/2684822.2685324
  • Rodríguez-López, M. E., Alcántara-Pilar, J. M., Del Barrio-García, S., & Muñoz-Leiva, F. (2020). A review of restaurant research in the last two decades: A bibliometric analysis. International Journal of Hospitality Management, 87, 102387. https://doi...
  • Ryoo, J. H., Wang, X., & Lu, S. (2021). Do Spoilers Really Spoil? Using Topic Modeling to Measure the Effect of Spoiler Reviews on Box Office Revenue. Journal of Marketing, 85(2), 70–88. https://doi.org/10.1177/0022242920937703
  • Ryu, K., & Han, H. (2009). Influence of the Quality of Food, Service, and Physical Environment on Customer Satisfaction and Behavioral Intention in Quick-Casual Restaurants: Moderating Role of Perceived Price. Journal of Hospitality & Tourism Research...
  • Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40. https://doi.org/10.1016/j.dss.2015.10.006
  • Samara, D., Magnisalis, I., & Peristeras, V. (2020). Artificial intelligence and big data in tourism: a systematic literature review. Journal of Hospitality and Tourism Technology, 11(2), 343–367. https://doi.org/10.1108/jhtt-12-2018-0118
  • Schmitt, M., Steinheber, S., Schreiber, K., & Roth, B. (2018). Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks. https://doi.org/10.18653/v1/d18-1139
  • Schubert, F., Kandampully, J., Solnet, D., & Kralj, A. (2010). Exploring Consumer Perceptions of Green Restaurants in the US. Tourism and Hospitality Research, 10(4), 286–300. https://doi.org/10.1057/thr.2010.17
  • Seo, S. (2017). Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network. https://www.semanticscholar.org/paper/Representation-Learning-of-Users-and-Items-for-Seo-Huang/494689f4522619b8...
  • Séraphin, H., & Gowreesunkar, V. (2020). Children in Hospitality and Tourism: Marketing and Managing Experiences. Walter de Gruyter GmbH & Co KG.
  • Shacklett, M. (2022). Unstructured data: A cheat sheet. TechRepublic. https://www.techrepublic.com/article/unstructured-data-the-smart-persons-guide/
  • Shah, D., & Murthi, B. P. S. (2021). Marketing in a data-driven digital world: Implications for the role and scope of marketing. Journal of Business Research, 125, 772–779. https://doi.org/10.1016/j.jbusres.2020.06.062
  • Shapoval, V., Murphy, K., & Severt, D. (2018). Does service quality really matter at Green restaurants for Millennial consumers? The moderating effects of gender between loyalty and satisfaction. Journal of Foodservice Business Research, 21(6), 591–60...
  • Sharif, O., Hoque, M. M., & Hossain, E. (2019). Sentiment Analysis of Bengali Texts on Online Restaurant Reviews Using Multinomial Naïve Bayes. In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)....
  • Sheth, J. N. (2021). New areas of research in marketing strategy, consumer behavior, and marketing analytics: the future is bright. The Journal of Marketing Theory and Practice, 29(1), 3–12. https://doi.org/10.1080/10696679.2020.1860679
  • Sheth, J. N., & Kellstadt, C. H. (2021). Next frontiers of research in data driven marketing: Will techniques keep up with data tsunami? Journal of Business Research, 125, 780–784. https://doi.org/10.1016/j.jbusres.2020.04.050
  • Sirianni, N. J., Bitner, M. J., Brown, S. J., & Mandel, N. (2013). Branded Service Encounters: Strategically Aligning Employee Behavior with the Brand Positioning. Journal of Marketing, 77(6), 108–123. https://doi.org/10.1509/jm.11.0485
  • Sivarajah, U., Kamal, M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001
  • Slack, N. J., Singh, G., Ali, J., Lata, R., Mudaliar, K., & Swamy, Y. (2020). Influence of fast-food restaurant service quality and its dimensions on customer perceived value, satisfaction and behavioural intentions. British Food Journal, 123(4), 1324...
  • Song, H., Qiu, R. L., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338–362. https://doi.org/10.1016/j....
  • Sparks, B., & Browning, V. L. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310–1323. https://doi.org/10.1016/j.tourman.2010.12.011
  • Srivastava, V., & Kalro, A. D. (2019). Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors. Journal of Interactive Marketing, 48, 33–50. https://doi.org/10.1016/j.intmar.2018.12.003
  • Steyvers, M., Smyth, P., & Chemuduganta, C. (2010). Combining Background Knowledge and Learned Topics. Topics in Cognitive Science, 3(1), 18–47. https://doi.org/10.1111/j.1756-8765.2010.01097.x
  • Sulek, J. M., & Hensley, R. L. (2004). The Relative Importance of Food, Atmosphere, and Fairness of Wait. Cornell Hotel and Restaurant Administration Quarterly, 45(3), 235–247. https://doi.org/10.1177/0010880404265345
  • Sun, C., Huang, L., & Qiu, X. (2019). Utilizing. https://doi.org/10.18653/v1/n19-1035
  • Sustainability. (n.d.). NRA. https://restaurant.org/education-and-resources/learning-center/business-operations/sustainability/
  • Tajeddini, K., Kaur, P., Ferraris, A., & Dhir, A. (2021). What motivates the adoption of green restaurant products and services? A systematic review and future research agenda. Business Strategy and the Environment, 30(4), 2224–2240. https://doi.org/1...
  • Tan, B. C., Lau, T., Yong, G. F. D., Khan, N. I., & Nguyen, T. H. O. (2019). A qualitative study of green practices adoption for restaurants in Malaysia. Social Responsibility Journal, 15(8), 1087–1099. https://doi.org/10.1108/srj-07-2017-0119
  • Tan, B., & Yeap, P. F. (2012). What Drives Green Restaurant Patronage Intention? International Journal of Business and Management, 7(2). https://doi.org/10.5539/ijbm.v7n2p215
  • Tang, D., Qin, B., Liu, T., & Yang, Y. (2015). User modeling with neural network for review rating prediction. In International Conference on Artificial Intelligence (pp. 1340–1346). https://www.ijcai.org/Proceedings/15/Papers/193.pdf
  • Tanrisevdi, A., Öztürk, G., & Öztürk, A. (2022). A supervised data mining approach for predicting comment card ratings. International Journal of Contemporary Hospitality Management, 34(5), 1823–1853. https://doi.org/10.1108/ijchm-05-2021-0675
  • Tao, J., & Fang, X. (2020). Toward multi-label sentiment analysis: a transfer learning based approach. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-019-0278-0
  • Tian, G., Lu, L., & McIntosh, C. H. (2021). What factors affect consumers’ dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, 104060. https://doi.org/10.1016/j.foodqual.2020.104060
  • Timoshenko, A., & Hauser, J. (2019). Identifying Customer Needs from User-Generated Content. Marketing Science, 38(1), 1–20. https://doi.org/10.1287/mksc.2018.1123
  • Tirunillai, S., & Tellis, G. J. (2014). Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation. Journal of Marketing Research, 51(4), 463–479. https://doi.org/10.1509/jmr.12.0106
  • Torres, E. N., Singh, D., & Robertson-Ring, A. (2015). Consumer reviews and the creation of booking transaction value: Lessons from the hotel industry. International Journal of Hospitality Management, 50, 77–83. https://doi.org/10.1016/j.ijhm.2015.07.012
  • Total data volume worldwide 2010-2025 | Statista. (2022, September 8). https://www.statista.com/statistics/871513/worldwide-data-created/
  • Trafialek, J., Czarniecka-Skubina, E., Kulaitiené, J., & Vaitkevičienė, N. (2019). Restaurant’s Multidimensional Evaluation Concerning Food Quality, Service, and Sustainable Practices: A Cross-National Case Study of Poland and Lithuania. Sustainabilit...
  • Trang, H. L. T., Lee, J. Y., & Han, H. (2019). How do green attributes elicit pro-environmental behaviors in guests? The case of green hotels in Vietnam. Journal of Travel & Tourism Marketing, 36(1), 14–28. https://doi.org/10.1080/10548408.2018.1486782
  • Trisna, K. W., & Jie, H. (2022). Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review. Applied Artificial Intelligence, 36(1). https://doi.org/10.1080/08839514.2021.2014186
  • Tsao, H., Campbell, C., Sands, S., Ferraro, C., Mavrommatis, A., & Lu, S. S. Y. (2019). A machine-learning based approach to measuring constructs through text analysis. European Journal of Marketing, 54(3), 511–524. https://doi.org/10.1108/ejm-01-2019...
  • Tsao, H., Chen, M., Campbell, C., & Sands, S. (2020). Estimating numerical scale ratings from text-based service reviews. Journal of Service Management, 31(2), 187–202. https://doi.org/10.1108/josm-06-2019-0167
  • Uddin, M. (2019). Customer loyalty in the fast food restaurants of Bangladesh. British Food Journal, 121(11), 2791–2808. https://doi.org/10.1108/bfj-02-2019-0140
  • Valdivia, A. Y., Hrabova, E., Chaturvedi, I., Luzón, M. V., Troiano, L., Cambria, E., & Herrera, F. (2019). Inconsistencies on TripAdvisor reviews: A unified index between users and Sentiment Analysis Methods. Neurocomputing, 353, 3–16. https://doi.or...
  • Van Calster, B., Verbakel, J. Y., Christodoulou, E., Steyerberg, E. W., & Collins, G. (2019). Statistics versus machine learning: definitions are interesting (but understanding, methodology, and reporting are more important). Journal of Clinical Epide...
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is All you Need. In arXiv (Cornell University) (Vol. 30, pp. 5998–6008). Cornell University. https://arxiv.org/pdf/1706.03762v5
  • Vayansky, I., & Kumar, S. (2020). A review of topic modeling methods. Information Systems, 94, 101582. https://doi.org/10.1016/j.is.2020.101582
  • Vazan, M. (2021, September 16). Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews. arXiv.org. http://arxiv.org/abs/2109.07680
  • Vazan, M., Masoumi, F., & Majd, S. S. (2022). A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2201.06313
  • Verain, M., Dagevos, H., & Antonides, G. (2015). Sustainable food consumption. Product choice or curtailment? Appetite, 91, 375–384. https://doi.org/10.1016/j.appet.2015.04.055
  • Wall, E., & Berry, L. L. (2007). The Combined Effects of the Physical Environment and Employee Behavior on Customer Perception of Restaurant Service Quality. Cornell Hotel and Restaurant Administration Quarterly, 48(1), 59–69. https://doi.org/10.1177/...
  • Wan, H., Yang, Y., Du, J., Liu, Y., Qi, K., & Pan, J. Z. (2020). Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis. Proceedings of the . . . AAAI Conference on Artificial Intelligence, 34(05), 9122–9129. https://doi.org/10.16...
  • Wang, J., Li, Y., Wu, B., & Wang, Y. (2020). Tourism destination image based on tourism user generated content on internet. Tourism Review, 76(1), 125–137. https://doi.org/10.1108/tr-04-2019-0132
  • Wang, S., Li, J., & Yang, F. (2019). Do motivations contribute to local residents’ engagement in pro-environmental behaviors? Resident-destination relationship and pro-environmental climate perspective. Journal of Sustainable Tourism, 28(6), 834–852. ...
  • Wang, X., Bendle, N., Mai, F., & Cotte, J. (2015). TheJournal of Consumer Researchat 40: A Historical Analysis. Journal of Consumer Research, 42(1), 5–18. https://doi.org/10.1093/jcr/ucv009
  • Wang, Y. (2022, May 2). How Support for Black Lives Matter Impacts Consumer Responses on Social Media. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4099045
  • Wang, Y., Kim, J., & Kim, J. (2021). The financial impact of online customer reviews in the restaurant industry: A moderating effect of brand equity. International Journal of Hospitality Management, 95, 102895. https://doi.org/10.1016/j.ijhm.2021.102895
  • Wang, Y., Sun, A., Huang, M., & Zhu, X. (2019). Aspect-level Sentiment Analysis using AS-Capsules. https://doi.org/10.1145/3308558.3313750
  • Wankhade, M., Rao, A. C. S., & Kulkarni, C. A. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1
  • Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach. Cengage Learning.
  • Wu, H., Cheng, C., & Ai, C. (2019). What drives green experiential loyalty towards green restaurants? Tourism Review, 76(5), 1084–1103. https://doi.org/10.1108/tr-02-2019-0079
  • Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51–65. https://doi.org/10.1016/j.tourman.2016.10.001
  • Xie, Y., Ma, W., & Tong, Z. (2023). How counterfactual thinking affects willingness to consume green restaurant products: Mediating role of regret and moderating role of COVID-19 risk perception. Journal of Hospitality and Tourism Management, 55, 344–...
  • Xu, F., Nash, N. A., & Whitmarsh, L. (2019). Big data or small data? A methodological review of sustainable tourism. Journal of Sustainable Tourism, 28(2), 144–163. https://doi.org/10.1080/09669582.2019.1631318
  • Xu, X. (2018). Examining the Relevance of Online Customer Textual Reviews on Hotels’ Product and Service Attributes. Journal of Hospitality & Tourism Research, 43(1), 141–163. https://doi.org/10.1177/1096348018764573
  • Xu, X. (2020). Examining an asymmetric effect between online customer reviews emphasis and overall satisfaction determinants. Journal of Business Research, 106, 196–210. https://doi.org/10.1016/j.jbusres.2018.07.022
  • Xu, X. (2021). What are customers commenting on, and how is their satisfaction affected? Examining online reviews in the on-demand food service context. Decision Support Systems, 142, 113467. https://doi.org/10.1016/j.dss.2020.113467
  • Xuan, H. N. T., Hieu, V. M. N., & Le, A. (2018). Adding External Features to Convolutional Neural Network for Aspect-based Sentiment Analysis. https://doi.org/10.1109/nics.2018.8606820
  • Yadav, R., Dokania, A. K., & Pathak, G. (2016). The influence of green marketing functions in building corporate image. International Journal of Contemporary Hospitality Management, 28(10), 2178–2196. https://doi.org/10.1108/ijchm-05-2015-0233
  • Yan, X., Wang, J., & Chau, M. (2015). Customer revisit intention to restaurants: Evidence from online reviews. Information Systems Frontiers, 17(3), 645–657. https://doi.org/10.1007/s10796-013-9446-5
  • Yoo, S., Song, J., & Jeong, O. (2018). Social media contents based sentiment analysis and prediction system. Expert Systems With Applications, 105, 102–111. https://doi.org/10.1016/j.eswa.2018.03.055
  • Yu, Y., Luo, M., & Zhu, D. (2018). The Effect of Quality Attributes on Visiting Consumers’ Patronage Intentions of Green Restaurants. Sustainability, 10(4), 1187. https://doi.org/10.3390/su10041187
  • Zablocki, A., Schlegelmilch, B. B., & Houston, M. J. (2018). How valence, volume and variance of online reviews influence brand attitudes. AMS Review, 9(1–2), 61–77. https://doi.org/10.1007/s13162-018-0123-1
  • Zahoor, K., Bawany, N. Z., & Hamid, S. (2020). Sentiment Analysis and Classification of Restaurant Reviews using Machine Learning. https://doi.org/10.1109/acit50332.2020.9300098
  • Zaki, M. H., & McColl-Kennedy, J. R. (2020). Text mining analysis roadmap (TMAR) for service research. Journal of Services Marketing, 34(1), 30–47. https://doi.org/10.1108/jsm-02-2019-0074
  • Zhang, J. (2019). What’s yours is mine: exploring customer voice on Airbnb using text-mining approaches. Journal of Consumer Marketing, 36(5), 655–665. https://doi.org/10.1108/jcm-02-2018-2581
  • Zhang, S. (2018). A Recommender System for Cultural Restaurants Based on Review Factors and Review Sentiment. https://www.semanticscholar.org/paper/A-Recommender-System-for-Cultural-Restaurants-Based-Zhang-Salehan/223ff59d1acfbb6fd63c12bf258c1edbf4f224ba
  • Zhang, S., Ly, L., Mach, N., & Amaya, C. H. (2022). Topic Modeling and Sentiment Analysis of Yelp Restaurant Reviews. International Journal of Information Systems in the Service Sector, 14(1), 1–16. https://doi.org/10.4018/ijisss.295872
  • Zhang, W., Li, X., Deng, Y., Bing, L., & Lam, W. (2022). A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2203.01054
  • Zhang, Z., Zhang, Z., & Law, R. (2012). Positive and Negative Word of Mouth about Restaurants: Exploring the Asymmetric Impact of the Performance of Attributes. Asia Pacific Journal of Tourism Research, 19(2), 162–180. https://doi.org/10.1080/10941665...
  • Zhao, W., Chen, J. M., Perkins, R., Liu, Z., Ge, W., Ding, Y., & Zou, W. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics, 16(S13). https://doi.org/10.1186/1471-2105-16-s13-s8
  • Zheng, T., Wu, F., Law, R., Qiu, Q., & Wu, R. (2021). Identifying unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting approach. International Journal of Hospitality Management, 92, 102658. https://doi.org/...
  • Zhu, L., Lin, Y., & Cheng, M. (2020). Sentiment and guest satisfaction with peer-to-peer accommodation: When are online ratings more trustworthy? International Journal of Hospitality Management, 86, 102369. https://doi.org/10.1016/j.ijhm.2019.102369
  • Zibarzani, M., Abumalloh, R. A., Nilashi, M., Samad, S., Alghamdi, O., Nayer, F., Ismail, M., Mohd, S., & Akib, N. a. M. (2022). Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology. Technology in So...
• چکیده فارسی

...see more