Research Article
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Year 2024, , 254 - 286, 05.09.2024
https://doi.org/10.30519/ahtr.1436175

Abstract

References

  • Afzaal, M., Usman, M., & Fong, A. (2019). Tourism mobile app with aspect-based sentiment classification framework for tourist reviews. IEEE Transactions on Consumer Electronics, 65(2), 233-242.
  • Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175–191.
  • Alegre, J., & Garau, J. (2010). Tourist satisfaction and dissatisfaction. Annals of Tourism Research, 37(1), 52-73.
  • Ali, T., Marc, B., Omar, B., Soulaimane, K., & Larbi, S. (2021). Exploring destination’s negative e-reputation using aspect based sentiment analysis approach: Case of Marrakech destination on Tripadvisor. Tourism Management Perspectives, 40, 100892.
  • Ansar, W., Goswami, S., Chakrabarti, A., & Chakraborty, B. (2021). An efficient methodology for aspect-based sentiment analysis using BERT through refined aspect extraction. Journal of Intelligent & Fuzzy Systems, 40(5), 9627–9644.
  • Bianchi, C. (2016). Solo holiday travellers: Motivators and drivers of satisfaction and dissatisfaction. International Journal of Tourism Research, 18(2), 197-208.
  • Calderón-Fajardo, V., Anaya-Sánchez, R., & Molinillo, S. (2024). Understanding destination brand experience through data mining and machine learning. Journal of Destination Marketing & Management, 31, 100862
  • Carenini, G., Cheung, J. C. K., & Pauls, A. (2013). Multi-document summarization of evaluative text. Computational Intelligence, 29(4), 545–576.
  • Chan, J. K. L., & Baum, T. (2007). Determination of satisfiers and dissatisfiers using Herzberg’s motivator and hygiene factor theory: An exploratory study. Tourism Culture & Communication, 7(2), 117–131.
  • Chauhan, G. S., Kumar Meena, Y., Gopalani, D., & Nahta, R. (2020). A two-step hybrid unsupervised model with attention mechanism for aspect extraction. Expert Systems with Applications, 161, 113673.
  • Chu, M., Chen, Y., Yang, L., & Wang, J. (2022). Language interpretation in travel guidance platform: Text mining and sentiment analysis of Tripadvisor reviews. Frontiers in Psychology, 13, 1029945.
  • Di Fabbrizio, G., Stent, A., & Gaizauskas, R. (2014). A hybrid approach to multi-document summarization of opinions in reviews. In Proceedings of the 8th international natural language generation conference (inlg) (pp. 54–63).
  • Dragoni, M., Federici, M., & Rexha, A. (2019). An unsupervised aspect extraction strategy for monitoring real-time reviews stream. Information Processing & Management, 56(3), 1103-1118.
  • Ekinci, E., & Ilhan Omurca, S. (2020). Concept-lda: Incorporating babelfy into lda for aspect˙ extraction. Journal of Information Science, 46(3), 406-418.
  • Fernandes, T., & Fernandes, F. (2018). Sharing dissatisfaction online: Analyzing the nature and predictors of hotel guests negative reviews. Journal of Hospitality Marketing & Management, 27(2), 127-150.
  • Gerani, S., Carenini, G., & Ng, R. T. (2019). Modeling content and structure for abstractive review summarization. Computer Speech & Language, 53, 302-331. https://doi.org/10.1016/j.csl.2016.06.005
  • Ghosal, S., & Jain, A. (2023). Weighted aspect based sentiment analysis using extended OWA operators and Word2Vec for tourism. Multimedia Tools and Applications, 82(12), 18353–18380.
  • He, J., Li, L., Wang, Y., & Wu, X. (2021). Hierarchical features-based targeted aspect extraction from online reviews. Intelligent Data Analysis, 25(1), 205-223.
  • Hu, M., & Liu, B. (2004). Mining opinion features in customer reviews. In Aaai (Vol. 4, pp. 755–760).
  • 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.
  • Jang, Y. J., Cho, S.-B., & Kim, W. G. (2013). Effect of restaurant patrons’ regret and disappointment on dissatisfaction and behavioral intention. Journal of Travel & Tourism Marketing, 30(5), 431-444.
  • Jiang, S., Moyle, B., Yung, R., Tao, L., & Scott, N. (2022). Augmented reality and the enhancement of memorable tourism experiences at heritage sites. Current Issues in Tourism, 26(2), 242-257.
  • Kim, S. S., Shin, W., & Kim, H.-W. (2024). Unravelling long-stay tourist experiences and satisfaction: text mining and deep learning approaches. Current Issues in Tourism, 1–19. https://doi.org/10.1080/13683500.2024.2327840
  • Kim, Y.-J., & Kim, H.-S. (2022). The impact of hotel customer experience on customer satisfaction through online reviews. Sustainability, 14(2), 848.
  • Kuhzady, S., & Ghasemi, V. (2019). Factors influencing customers’ satisfaction and dissatisfaction with hotels: A text-mining approach. Tourism Analysis, 24(1), 69-79.
  • Kumar, A., Saini, M., & Sharan, A. (2020). Aspect category detection using statistical and semantic association. Computational Intelligence, 36(3), 1161-1182.
  • Lam-González, Y. E., Clouet, R., Cruz Sosa, N., & de Le´on, J. (2021). Dissatisfaction responses of tourists in the Havana world heritage site. Sustainability, 13(19), 11015.
  • Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., et al. (2019). BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. CoRR, abs/1910.13461. https://doi.org/10.48550/arXiv.1910.13461
  • Li, H., Yu, B. X., Li, G., & Gao, H. (2023). Restaurant survival prediction using customer generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 104707.
  • Liu, B. (2010). Sentiment analysis and subjectivity. In Handbook of natural language processing (2nd edition) (pp. 627-666). Taylor and Francis Group.
  • Liu, B. (2015). Preface. In Sentiment analysis (pp. xi–xiv). Cambridge: Cambridge University Press.
  • Luo, Z., Huang, S., & Zhu, K. Q. (2019). Knowledge empowered prominent aspect extraction from product reviews. Information Processing & Management, 56(3), 408-423.
  • Ma, J., Li, F. S., & Shang, Y. (2022). Tourist scams, moral emotions and behaviors: impacts on moral emotions, dissatisfaction, revisit intention and negative word of mouth. Tourism Review, 77(5), 1299–1321.
  • Maity, A., Ghosh, S., Karfa, S., Mukhopadhyay, M., Pal, S., & Pramanik, P. K. D. (2020). Sentiment analysis from travelers’ reviews using enhanced conjunction rule-based approach for feature-specific evaluation of hotels. Journal of Statistics and Management Systems, 23(6), 983-997.
  • Mate, M. J., Trupp, A., & Pratt, S. (2019). Managing negative online accommodation reviews: evidence from the Cook Islands. Journal of Travel & Tourism Marketing, 36(5), 627644.
  • Mehra, P. (2023). Unexpected surprise: Emotion analysis and aspect based sentiment analysis (ABSA) of user generated comments to study behavioral intentions of tourists. Tourism Management Perspectives, 45, 101063.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. CoRR, abs/1301.3781. https://doi.org/10.48550/arXiv.1301.3781
  • Moreno-Ortiz, A., Salles-Bernal, S., & Orrequia-Barea, A. (2019). Design and validation of annotation schemas for aspect-based sentiment analysis in the tourism sector. Information Technology & Tourism, 21(4), 535–557.
  • Oh, S., Ji, H., Kim, J., Park, E., & del Pobil, A. P. (2022). Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Information Technology & Tourism, 24(1), 109–126.
  • Ozen, I. A., & Ozgul Katlav, E. (2023). Aspect-based sentiment analysis on online customer reviews: a case study of technology-supported hotels. Journal of Hospitality and Tourism Technology, 14(2), 102–120
  • Park, H., Lee, M., & Back, K.-J. (2021). Exploring the roles of hotel wellness attributes in customer satisfaction and dissatisfaction: application of Kano model through mixed methods. International Journal of Contemporary Hospitality Management, 33(1), 263– 285.
  • Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In Empirical methods in natural language processing (EMNLP) (pp. 1532–1543).
  • Polyzos, E., Fotiadis, A., & Huan, T.-C. (2024). The asymmetric impact of twitter sentiment and emotions: Impulse response analysis on European tourism firms using micro-data. Tourism Management, 104, 104909.
  • Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108, 42-49.
  • Prakash, S. L., Perera, P., Newsome, D., Kusuminda, T., & Walker, O. (2019). Reasons for visitor dissatisfaction with wildlife tourism experiences at highly visited national parks in Sri Lanka. Journal of Outdoor Recreation and Tourism, 25, 102-112.
  • Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... Liu, P. J. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv. Retrieved from https://arxiv.org/abs/1910.10683
  • Rauf, A. A., & Pasha, F. M. (2024). Vlogging gastronomic tourism: understanding global north-south dynamics in YouTube videos and their audiences’ feedback. Tourism Geographies, 26(3), 407–431.
  • Rodrigues, H., Brochado, A., & Troilo, M. (2020). Listening to the murmur of water: Essential satisfaction and dissatisfaction attributes of thermal and mineral spas. Journal of Travel & Tourism Marketing, 37(5), 649-661.
  • Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.
  • Sann, R., & Lai, P.-C. (2020). Understanding homophily of service failure within the hotel guest cycle: Applying nlp-aspect-based sentiment analysis to the hospitality industry. International Journal of Hospitality Management, 91, 102678.
  • Santos, B. N. D., Marcacini, R. M., & Rezende, S. O. (2021). Multi-domain aspect extraction using bidirectional encoder representations from transformers. IEEE Access, 9, 9160491613.
  • Schweter, S., & Akbik, A. (2020). Flert: Document-level features for named entity recognition.
  • Shahhosseini, M., & Nasr, A. K. (2024). What attributes affect customer satisfaction in green restaurants? an aspect-based sentiment analysis approach. Journal of Travel & Tourism Marketing, 41(4), 472–490
  • Speer, R., Chin, J., & Havasi, C. (2017). ConceptNet 5.5: An open multilingual graph of general knowledge. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1) (pp. 4444–4451).
  • Stepaniuk, K., & Sturgulewska, A. (2021). Hitchhiking experiences and perception of affective label polarity in social networking sites—potential memetic implications for digital visual content management. Sustainability, 13(1), 223.
  • Taheri, B., Olya, H., Ali, F., & Gannon, M. J. (2020). Understanding the influence of airport servicescape on traveler dissatisfaction and misbehavior. Journal of Travel Research, 59(6), 1008-1028.
  • Tang, G., & Zeng, H. (2021). Evaluation of tourism e-commerce user satisfaction. Journal of Organizational and End User Computing, 33(5), 25-41.
  • Thanyasunthornsakun, K. (2016). An evaluation of cultural heritage tourism destination attributes for delighting visitors: A case study of the Ban Chiang archaeological site. Pertanika Journal of Social Science and Humanities, 24(SI), 95-114.
  • Um, K.-H., & Kim, S.-M. (2018). Application of fairness theory to medical tourists’ dissatisfaction and complaint behaviors: The moderating role of patient participation in medical tourism. Journal of Social Service Research, 44(2), 191-208.
  • Valdivia, A., Martínez-Cámara, E., Chaturvedi, I., Luzón, M. V., Cambria, E., Ong, Y.-S., & Herrera, F. (2020). What do people think about this monument? understanding negative reviews via deep learning, clustering and descriptive rules. Journal of Ambient Intelligence and Humanized Computing, 11(1), 39–52.
  • Viñan-Ludeña, M.-S. (2019). A systematic literature review on social media analytics and smart tourism. In Smart tourism as a driver for culture and sustainability (pp. 357–374). Springer, Cham.
  • Viñán-Ludeña, M. S., de Campos, L. M., Jacome-Galarza, L. R., & Sinche-Freire, J. (2020). Social media influence: A comprehensive review in general and in tourism domain. In Smart innovation, systems and technologies (Vol. 171, pp. 25–35). Springer.
  • Viñán-Ludeña, M. S., & de Campos, L. M. (2022). Analyzing tourist data on twitter: A case study in the province of Granada at Spain. Journal of Hospitality and Tourism Insights, 5(2), 435–464.
  • Viñán-Ludeña, M. S., & de Campos, L. M. (2022). Discovering a tourism destination with social media data: BERT-based sentiment analysis. Journal of Hospitality and Tourism Technology, 13(5), 907–921.
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Evaluating Tourist Dissatisfaction with Aspect-Based Sentiment Analysis Using Social Media Data

Year 2024, , 254 - 286, 05.09.2024
https://doi.org/10.30519/ahtr.1436175

Abstract

Tourism satisfaction is essential for encouraging tourists to stay longer, spend more and return. However, visitor dissatisfaction can also prove useful for understanding any shortcomings of a tourist destination, and Twitter, Instagram and TripAdvisor reviews might be able to provide an insight into tourist perceptions and experiences. This study examines the major causes of tourist dissatisfaction with a tourism destination using an aspect-based sentiment analysis approach to understand the key points of negative tweets, posts or reviews. We examined 19,340 tweets, 7,712 Instagram posts and 25,483 reviews about Granada in Spain in order to evaluate the negative user's perceptions, discover management-related problems and provide feedback to destination management organizations to enable them to improve their services and operations. Our work contributes to computational methods to address tourism (dis)satisfaction with a process to identify the most important entities (places), an algorithm to identify aspects and opinions, and the use of word-trees to show the most important aspect-opinion tuples. In practical terms, we provide to tourism industry professionals and managers, as well as travelers, with methods to identify the reasons for tourist dissatisfaction from available social media data, in such a way that managerial strategies or travel plans can be improved.

References

  • Afzaal, M., Usman, M., & Fong, A. (2019). Tourism mobile app with aspect-based sentiment classification framework for tourist reviews. IEEE Transactions on Consumer Electronics, 65(2), 233-242.
  • Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175–191.
  • Alegre, J., & Garau, J. (2010). Tourist satisfaction and dissatisfaction. Annals of Tourism Research, 37(1), 52-73.
  • Ali, T., Marc, B., Omar, B., Soulaimane, K., & Larbi, S. (2021). Exploring destination’s negative e-reputation using aspect based sentiment analysis approach: Case of Marrakech destination on Tripadvisor. Tourism Management Perspectives, 40, 100892.
  • Ansar, W., Goswami, S., Chakrabarti, A., & Chakraborty, B. (2021). An efficient methodology for aspect-based sentiment analysis using BERT through refined aspect extraction. Journal of Intelligent & Fuzzy Systems, 40(5), 9627–9644.
  • Bianchi, C. (2016). Solo holiday travellers: Motivators and drivers of satisfaction and dissatisfaction. International Journal of Tourism Research, 18(2), 197-208.
  • Calderón-Fajardo, V., Anaya-Sánchez, R., & Molinillo, S. (2024). Understanding destination brand experience through data mining and machine learning. Journal of Destination Marketing & Management, 31, 100862
  • Carenini, G., Cheung, J. C. K., & Pauls, A. (2013). Multi-document summarization of evaluative text. Computational Intelligence, 29(4), 545–576.
  • Chan, J. K. L., & Baum, T. (2007). Determination of satisfiers and dissatisfiers using Herzberg’s motivator and hygiene factor theory: An exploratory study. Tourism Culture & Communication, 7(2), 117–131.
  • Chauhan, G. S., Kumar Meena, Y., Gopalani, D., & Nahta, R. (2020). A two-step hybrid unsupervised model with attention mechanism for aspect extraction. Expert Systems with Applications, 161, 113673.
  • Chu, M., Chen, Y., Yang, L., & Wang, J. (2022). Language interpretation in travel guidance platform: Text mining and sentiment analysis of Tripadvisor reviews. Frontiers in Psychology, 13, 1029945.
  • Di Fabbrizio, G., Stent, A., & Gaizauskas, R. (2014). A hybrid approach to multi-document summarization of opinions in reviews. In Proceedings of the 8th international natural language generation conference (inlg) (pp. 54–63).
  • Dragoni, M., Federici, M., & Rexha, A. (2019). An unsupervised aspect extraction strategy for monitoring real-time reviews stream. Information Processing & Management, 56(3), 1103-1118.
  • Ekinci, E., & Ilhan Omurca, S. (2020). Concept-lda: Incorporating babelfy into lda for aspect˙ extraction. Journal of Information Science, 46(3), 406-418.
  • Fernandes, T., & Fernandes, F. (2018). Sharing dissatisfaction online: Analyzing the nature and predictors of hotel guests negative reviews. Journal of Hospitality Marketing & Management, 27(2), 127-150.
  • Gerani, S., Carenini, G., & Ng, R. T. (2019). Modeling content and structure for abstractive review summarization. Computer Speech & Language, 53, 302-331. https://doi.org/10.1016/j.csl.2016.06.005
  • Ghosal, S., & Jain, A. (2023). Weighted aspect based sentiment analysis using extended OWA operators and Word2Vec for tourism. Multimedia Tools and Applications, 82(12), 18353–18380.
  • He, J., Li, L., Wang, Y., & Wu, X. (2021). Hierarchical features-based targeted aspect extraction from online reviews. Intelligent Data Analysis, 25(1), 205-223.
  • Hu, M., & Liu, B. (2004). Mining opinion features in customer reviews. In Aaai (Vol. 4, pp. 755–760).
  • 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.
  • Jang, Y. J., Cho, S.-B., & Kim, W. G. (2013). Effect of restaurant patrons’ regret and disappointment on dissatisfaction and behavioral intention. Journal of Travel & Tourism Marketing, 30(5), 431-444.
  • Jiang, S., Moyle, B., Yung, R., Tao, L., & Scott, N. (2022). Augmented reality and the enhancement of memorable tourism experiences at heritage sites. Current Issues in Tourism, 26(2), 242-257.
  • Kim, S. S., Shin, W., & Kim, H.-W. (2024). Unravelling long-stay tourist experiences and satisfaction: text mining and deep learning approaches. Current Issues in Tourism, 1–19. https://doi.org/10.1080/13683500.2024.2327840
  • Kim, Y.-J., & Kim, H.-S. (2022). The impact of hotel customer experience on customer satisfaction through online reviews. Sustainability, 14(2), 848.
  • Kuhzady, S., & Ghasemi, V. (2019). Factors influencing customers’ satisfaction and dissatisfaction with hotels: A text-mining approach. Tourism Analysis, 24(1), 69-79.
  • Kumar, A., Saini, M., & Sharan, A. (2020). Aspect category detection using statistical and semantic association. Computational Intelligence, 36(3), 1161-1182.
  • Lam-González, Y. E., Clouet, R., Cruz Sosa, N., & de Le´on, J. (2021). Dissatisfaction responses of tourists in the Havana world heritage site. Sustainability, 13(19), 11015.
  • Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., et al. (2019). BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. CoRR, abs/1910.13461. https://doi.org/10.48550/arXiv.1910.13461
  • Li, H., Yu, B. X., Li, G., & Gao, H. (2023). Restaurant survival prediction using customer generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 104707.
  • Liu, B. (2010). Sentiment analysis and subjectivity. In Handbook of natural language processing (2nd edition) (pp. 627-666). Taylor and Francis Group.
  • Liu, B. (2015). Preface. In Sentiment analysis (pp. xi–xiv). Cambridge: Cambridge University Press.
  • Luo, Z., Huang, S., & Zhu, K. Q. (2019). Knowledge empowered prominent aspect extraction from product reviews. Information Processing & Management, 56(3), 408-423.
  • Ma, J., Li, F. S., & Shang, Y. (2022). Tourist scams, moral emotions and behaviors: impacts on moral emotions, dissatisfaction, revisit intention and negative word of mouth. Tourism Review, 77(5), 1299–1321.
  • Maity, A., Ghosh, S., Karfa, S., Mukhopadhyay, M., Pal, S., & Pramanik, P. K. D. (2020). Sentiment analysis from travelers’ reviews using enhanced conjunction rule-based approach for feature-specific evaluation of hotels. Journal of Statistics and Management Systems, 23(6), 983-997.
  • Mate, M. J., Trupp, A., & Pratt, S. (2019). Managing negative online accommodation reviews: evidence from the Cook Islands. Journal of Travel & Tourism Marketing, 36(5), 627644.
  • Mehra, P. (2023). Unexpected surprise: Emotion analysis and aspect based sentiment analysis (ABSA) of user generated comments to study behavioral intentions of tourists. Tourism Management Perspectives, 45, 101063.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. CoRR, abs/1301.3781. https://doi.org/10.48550/arXiv.1301.3781
  • Moreno-Ortiz, A., Salles-Bernal, S., & Orrequia-Barea, A. (2019). Design and validation of annotation schemas for aspect-based sentiment analysis in the tourism sector. Information Technology & Tourism, 21(4), 535–557.
  • Oh, S., Ji, H., Kim, J., Park, E., & del Pobil, A. P. (2022). Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Information Technology & Tourism, 24(1), 109–126.
  • Ozen, I. A., & Ozgul Katlav, E. (2023). Aspect-based sentiment analysis on online customer reviews: a case study of technology-supported hotels. Journal of Hospitality and Tourism Technology, 14(2), 102–120
  • Park, H., Lee, M., & Back, K.-J. (2021). Exploring the roles of hotel wellness attributes in customer satisfaction and dissatisfaction: application of Kano model through mixed methods. International Journal of Contemporary Hospitality Management, 33(1), 263– 285.
  • Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In Empirical methods in natural language processing (EMNLP) (pp. 1532–1543).
  • Polyzos, E., Fotiadis, A., & Huan, T.-C. (2024). The asymmetric impact of twitter sentiment and emotions: Impulse response analysis on European tourism firms using micro-data. Tourism Management, 104, 104909.
  • Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108, 42-49.
  • Prakash, S. L., Perera, P., Newsome, D., Kusuminda, T., & Walker, O. (2019). Reasons for visitor dissatisfaction with wildlife tourism experiences at highly visited national parks in Sri Lanka. Journal of Outdoor Recreation and Tourism, 25, 102-112.
  • Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... Liu, P. J. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv. Retrieved from https://arxiv.org/abs/1910.10683
  • Rauf, A. A., & Pasha, F. M. (2024). Vlogging gastronomic tourism: understanding global north-south dynamics in YouTube videos and their audiences’ feedback. Tourism Geographies, 26(3), 407–431.
  • Rodrigues, H., Brochado, A., & Troilo, M. (2020). Listening to the murmur of water: Essential satisfaction and dissatisfaction attributes of thermal and mineral spas. Journal of Travel & Tourism Marketing, 37(5), 649-661.
  • Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.
  • Sann, R., & Lai, P.-C. (2020). Understanding homophily of service failure within the hotel guest cycle: Applying nlp-aspect-based sentiment analysis to the hospitality industry. International Journal of Hospitality Management, 91, 102678.
  • Santos, B. N. D., Marcacini, R. M., & Rezende, S. O. (2021). Multi-domain aspect extraction using bidirectional encoder representations from transformers. IEEE Access, 9, 9160491613.
  • Schweter, S., & Akbik, A. (2020). Flert: Document-level features for named entity recognition.
  • Shahhosseini, M., & Nasr, A. K. (2024). What attributes affect customer satisfaction in green restaurants? an aspect-based sentiment analysis approach. Journal of Travel & Tourism Marketing, 41(4), 472–490
  • Speer, R., Chin, J., & Havasi, C. (2017). ConceptNet 5.5: An open multilingual graph of general knowledge. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1) (pp. 4444–4451).
  • Stepaniuk, K., & Sturgulewska, A. (2021). Hitchhiking experiences and perception of affective label polarity in social networking sites—potential memetic implications for digital visual content management. Sustainability, 13(1), 223.
  • Taheri, B., Olya, H., Ali, F., & Gannon, M. J. (2020). Understanding the influence of airport servicescape on traveler dissatisfaction and misbehavior. Journal of Travel Research, 59(6), 1008-1028.
  • Tang, G., & Zeng, H. (2021). Evaluation of tourism e-commerce user satisfaction. Journal of Organizational and End User Computing, 33(5), 25-41.
  • Thanyasunthornsakun, K. (2016). An evaluation of cultural heritage tourism destination attributes for delighting visitors: A case study of the Ban Chiang archaeological site. Pertanika Journal of Social Science and Humanities, 24(SI), 95-114.
  • Um, K.-H., & Kim, S.-M. (2018). Application of fairness theory to medical tourists’ dissatisfaction and complaint behaviors: The moderating role of patient participation in medical tourism. Journal of Social Service Research, 44(2), 191-208.
  • Valdivia, A., Martínez-Cámara, E., Chaturvedi, I., Luzón, M. V., Cambria, E., Ong, Y.-S., & Herrera, F. (2020). What do people think about this monument? understanding negative reviews via deep learning, clustering and descriptive rules. Journal of Ambient Intelligence and Humanized Computing, 11(1), 39–52.
  • Viñan-Ludeña, M.-S. (2019). A systematic literature review on social media analytics and smart tourism. In Smart tourism as a driver for culture and sustainability (pp. 357–374). Springer, Cham.
  • Viñán-Ludeña, M. S., de Campos, L. M., Jacome-Galarza, L. R., & Sinche-Freire, J. (2020). Social media influence: A comprehensive review in general and in tourism domain. In Smart innovation, systems and technologies (Vol. 171, pp. 25–35). Springer.
  • Viñán-Ludeña, M. S., & de Campos, L. M. (2022). Analyzing tourist data on twitter: A case study in the province of Granada at Spain. Journal of Hospitality and Tourism Insights, 5(2), 435–464.
  • Viñán-Ludeña, M. S., & de Campos, L. M. (2022). Discovering a tourism destination with social media data: BERT-based sentiment analysis. Journal of Hospitality and Tourism Technology, 13(5), 907–921.
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There are 69 citations in total.

Details

Primary Language English
Subjects Tourism (Other)
Journal Section Research Article
Authors

Marlon Santiago Viñán-ludeña 0000-0003-2692-5899

Luis De Campos This is me 0000-0001-9125-1195

Early Pub Date July 8, 2024
Publication Date September 5, 2024
Submission Date February 13, 2024
Acceptance Date June 12, 2024
Published in Issue Year 2024

Cite

APA Viñán-ludeña, M. S., & De Campos, L. (2024). Evaluating Tourist Dissatisfaction with Aspect-Based Sentiment Analysis Using Social Media Data. Advances in Hospitality and Tourism Research (AHTR), 12(3), 254-286. https://doi.org/10.30519/ahtr.1436175


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