참고문헌
- Abril, D. (2020). Airbnb's IPO filing reveals huge COVID impact. Fortune. Retrieved from https://fortune.com/2020/11/16/airbnb-ipo-initial-public-offering-coronavirus-impact/
- Alhojely, S. (2016). Sentiment analysis and opinion mining: A survey. International Journal of Computer Applications, 150(6), 22-25. https://doi.org/10.5120/ijca2016911545
- Ayanso, A., Herath, T. C., and O'Brien, N. (2015). Understanding continuance intentions of physicians with electronic medical records (EMR): An expectancy-confirmation perspective. Decision Support Systems, 77, 112-122. https://doi.org/10.1016/j.dss.2015.06.003
- Baptista, M. L., Goebel, K., and Henriques, E. M. P. (2022). Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Artificial Intelligence, 306, 103667. https://doi.org/10.1016/j.artint.2022.103667
- Bastani, K., Namavari, H., and Shaffer, J. (2019). Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints. Expert Systems with Applications, 127, 256-271. https://doi.org/10.48550/arXiv.1807.07468
- Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
- Bickart, B., and Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of Interactive Marketing, 15(3), 31-40. https://doi.org/10.1002/dir.1014
- Blei, D. M., Ng, A. Y., and Edu, J. B. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
- de Oliveira Capela, F., and Ramirez-Marquez, J. E. (2019). Detecting urban identity perception via newspaper topic modeling. Cities, 93, 72-83. https://doi.org/10.1016/j.cities.2019.04.009
- Esuli, A., and Sebastiani, F. (2006). SENTIWORDNET: A publicly available lexical resource for opinion mining. Proceedings of the 5th International Conference on Language Resources and Evaluation, LREC 2006, 417-422. Retrieved from http://www.lrec-conf.org/proceedings/lrec2006/pdf/384_pdf.pdf
- Fullerton, L. (2017). Online reviews impact purchasing decisions for over 93% of consumers, report suggests, Retrieved from https://www.thedrum.com/news/2017/03/27/online-reviews-impact-purchasing-decisions-over-93-consumers-report-suggests
- Futagami, K., Fukazawa, Y., Kapoor, N., and Kito, T. (2021). Pairwise acquisition prediction with SHAP value interpretation. Journal of Finance and Data Science, 7, 22-44. https://doi.org/10.1016/j.jfds.2021.02.001
- Geetha, M., Singha, P., and Sinha, S. (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
- Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., and Yang, G. Z. (2019). XAI-Explainable artificial intelligence. Science Robotics, 4(37). https://doi.org/10.1126/scirobotics.aay7120
- Guzman, E., Azocar, D., and Li, Y. (2014). Sentiment analysis of commit comments in GitHub: An empirical study. 11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings, 352-355. https://doi.org/10.1145/2597073.2597118
- Hasan, M., Rahman, A., Karim, M. R., Khan, M. S. I., and Islam, M. J. (2021). Normalized approach to find optimal number of topics in Latent Dirichlet Allocation (LDA). Advances in Intelligent Systems and Computing, 1309, 341-354. https://doi.org/10.1007/978-981-33-4673-4_27
- Hong, L., and Davison, B. D. (2010). Empirical study of topic modeling in Twitter. SOMA 2010 - Proceedings of the 1st Workshop on Social Media Analytics, 80-88. https://doi.org/10.1145/1964858.1964870
- Hsupeng, B., Lee, K. W., Wei, T. E., and Wang, S. H. (2022). Explainable malware detection using predefined network flow. 2022 24th International Conference on Advanced Communication Technology (ICACT), 27-33. https://doi.org/10.23919/ICACT53585.2022.9728897
- Hu, F., Teichert, T., Deng, S., Liu, Y., and Zhou, G. (2021). Dealing with pandemics: An investigation of the effects of COVID-19 on customers' evaluations of hospitality services. Tourism Management, 85, 104320. https://doi.org/10.1016/j.tourman.2021.104320
- Hu, N., Bose, I., Koh, N. S., and Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674-684. https://doi.org/10.1016/j.dss.2011.11.002
- Hu, Y., Boyd-Graber, J., Satinoff, B., and Smith, A. (2014). Interactive topic modeling. Machine Learning, 95(3), 423-469. https://doi.org/10.1007/s10994-013-5413-0
- Huang, A. H., Lehavy, R., Zang, A. Y., and Zheng, R. (2018). Analyst information discovery and interpretation roles: A topic modeling approach. Management Science, 64(6), 2833-2855. https://doi.org/10.1287/mnsc.2017.2751
- Hutto, C. J., and Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014, 216-225. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14550
- Ilyas, S. H. W., Soomro, Z. T., Anwar, A., Shahzad, H., and Yaqub, U. (2020). Analyzing brexit's impact using sentiment analysis and topic modeling on twitter discussion. ACM International Conference Proceeding Series, 1-6. https://doi.org/10.1145/3396956.3396973
- Jeong, S. Y., Kim, J. W., Kim, Y. S., Joo, H. Y., and Moon, J. H. (2021). Sentiment analysis of nuclear energy-related articles and their comments on a portal site in Rep. of Korea in 2010-2019. Nuclear Engineering and Technology, 53(3), 1013-1019. https://doi.org/10.1016/j.net.2020.07.031
- Khanthaapha, P., Pipanmaekaporn, L., and Kamonsantiroj, S. (2018). Topic-based user profile model for POI recommendations. ACM International Conference Proceeding Series, 143-147. https://doi.org/10.1145/3206185.3206203
- Kim, E. H. J., Jeong, Y. K., Kim, Y., Kang, K. Y., and Song, M. (2016). Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news. Journal of Information Science, 42(6), 763-781. https://doi.org/10.1177/0165551515608733
- Kwon, H. J., Ban, H. J., Jun, J. K., and Kim, H. S. (2021). Topic modeling and sentiment analysis of online review for airlines. Information 2021, 12(2), 78. https://doi.org/10.3390/info12020078
- Lee, C. K. H. (2022). How guest-host interactions affect consumer experiences in the sharing economy: New evidence from a configurational analysis based on consumer reviews. Decision Support Systems, 152, 113634. https://doi.org/10.1016/j.dss.2021.113634
- Lee, H., Noh, E. B., Park, S. J., Nam, H. K., Lee, T. H., Lee, G. R., and Nam, E. W. (2021). COVID-19 vaccine perception in South Korea: Web crawling approach. JMIR Public Health and Surveillance, 7(9), e31409. https://doi.org/10.2196/31409
- Lee, M., Jeong, M., and Lee, J. (2017). Roles of negative emotions in customers' perceived helpfulness of hotel reviews on a user-generated review website: A text mining approach. International Journal of Contemporary Hospitality Management, 29(2), 762-783. https://doi.org/10.1108/IJCHM-10-2015-0626
- Liao, C., Lin, H. N., Luo, M. M., and Chea, S. (2017). Factors influencing online shoppers' repurchase intentions: The roles of satisfaction and regret. Information & Management, 54(5), 651-668. https://doi.org/10.1016/j.im.2016.12.005
- Liu, A. X., Li, Y., and Xu, S. X. (2021). Assessing the unacquainted: Inferred reviewer personality and review helpfulness. MIS Quarterly: Management Information Systems, 45(3), 1113-1148. https://doi.org/10.25300/MISQ/2021/14375
- Lu, Y., Hu, X., Wang, F., Kumar, S., Liu, H., and Maciejewski, R. (2015). Visualizing social media sentiment in disaster scenarios. WWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web, 1211-1215. https://doi.org/10.1145/2740908.2741720
- Lu, Y., and Zheng, Q. (2021). Twitter public sentiment dynamics on cruise tourism during the COVID-19 pandemic. Current Issues in Tourism, 24(7), 892-898. https://doi.org/10.1080/13683500.2020.1843607
- Lundberg, S. M., and Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-Dec., 4766-4775. https://doi.org/10.48550/arXiv.1705.07874
- Maheswaran, D., Mackie, D. M., and Chaiken, S. (1992). Brand name as a heuristic cue: The effects of task importance and expectancy confirmation on consumer judgments. Journal of Consumer Psychology, 1(4), 317-336. https://doi.org/10.1016/S1057-7408(08)80058-7
- McIlroy, S., Ali, N., Khalid, H., and E. Hassan, A. (2016). Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews. Empirical Software Engineering, 21(3), 1067-1106. https://doi.org/10.1007/s10664-015-9375-7
- Mehta, M. P., Kumar, G., and Ramkumar, M. (2021). Customer expectations in the hotel industry during the COVID-19 pandemic: A global perspective using sentiment analysis. Tourism Recreation Research. https://doi.org/10.1080/02508281.2021.1894692
- Mohammed, S. H., and Al-Augby, S. (2020). LSA & LDA topic modeling classification: Comparison study on E-books. Indonesian Journal of Electrical Engineering and Computer Science, 19(1), 353-362. http://doi.org/10.11591/ijeecs.v19.i1.pp353-362
- Mostafa, M. M. (2013). More than words: Social networks' text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241-4251. https://doi.org/10.1016/j.eswa.2013.01.019
- Nikolenko, S. I., Koltcov, S., and Koltsova, O. (2017). Topic modelling for qualitative studies. Article Journal of Information Science, 43(1), 88-102. https://doi.org/10.1177/016555151561739
- Ohana, B., and Tierney, B. (2009). Sentiment classification of reviews using SentiWordNet. 9th. IT & T Conference. https://doi.org/10.21427/D77S56
- Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460. https://doi.org/10.1177/002224378001700405
- Ramteke, J., Shah, S., Godhia, D., and Shaikh, A. (2016). Election result prediction using Twitter sentiment analysis. Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016, 1. https://doi.org/10.1109/INVENTIVE.2016.7823280
- Saifee, D. H., Zheng, Z., Bardhan, I. R., and Lahiri, A. (2020). Are online reviews of physicians reliable indicators of clinical outcomes? A focus on chronic disease management. Information Systems Research, 31(4), 1282-1300. https://doi.org/10.1287/isre.2020.0945
- Shim, J. G., Ryu, K. H., Lee, S. H., Cho, E. A., Lee, Y. J., and Ahn, J. H. (2021). Text mining approaches to analyze public sentiment changes regarding covid-19 vaccines on social media in korea. International Journal of Environmental Research and Public Health, 18(12), 6549. https://doi.org/10.3390/ijerph18126549
- Shin, D. H. (2011). Understanding e-book users: Uses and gratification expectancy model. New Media and Society, 13(2), 260-278. https://doi.org/10.1177/1461444810372163
- Sirbu, D., Secui, A., Dascalu, M., Crossley, S. A., Ruseti, S., and Trausan-Matu, S. (2017). Extracting gamers' opinions from reviews. Proceedings - 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2016, 227-232. http://doi.org/10.1109/SYNASC.2016.044
- Stieglitz, S., and Dang-Xuan, L. (2013). Emotions and information diffusion in social media - Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217-248. https://doi.org/10.2753/MIS0742-1222290408
- Sutherland, I., and Kiatkawsin, K. (2020). Determinants of guest experience in Airbnb: A topic modeling approach using LDA. Sustainability (Switzerland), 12(8). https://doi.org/10.3390/su12083402
- Sutherland, I., Sim, Y., Lee, S. K., Byun, J., and Kiatkawsin, K. (2020). Topic modeling of online accommodation reviews via latent dirichlet allocation. Sustainability (Switzerland), 12(5), 1-15. https://doi.org/10.3390/su12051821
- Syed, S., and Spruit, M. (2017). Full-Text or abstract? Examining topic coherence scores using latent dirichlet allocation. 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017, 2018-Jan., 165-174. http://doi.org/10.1109/DSAA.2017.61
- Taboada, M., Brooke, J., Tofiloski, M., Voll, K., and Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267-307. https://doi.org/10.1162/COLI_a_00049
- Thelwall, M. (2017). The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength (pp. 119-134). Springer, Cham.
- Thomsen, C. (R.), and Jeong, M. (2021). An analysis of Airbnb online reviews: user experience in 16 U.S. cities. Journal of Hospitality and Tourism Technology, 12(1), 97-111. https://doi.org/10.1108/JHTT-02-2019-0023
- Wang, L., and Kirilenko, A. P. (2021). Do tourists from different countries interpret travel experience with the same feeling? sentiment analysis of tripadvisor reviews. In Information and Communication Technologies in Tourism 2021 (pp. 294-301). Springer, Cham. https://doi.org/10.1007/978-3-030-65785-7_27
- Xiang, Z., Du, Q., Ma, Y., and 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
- Zhang, J. (2019). Listening to the consumer: exploring review topics on airbnb and their impact on listing performance. Journal of Marketing Theory and Practice, 27(4), 371-389. https://doi.org/10.1080/10696679.2019.1644953
- Zhang, J., and Piramuthu, S. (2018). Product recommendation with latent review topics. Information Systems Frontiers, 20(3), 617-625. https://doi.org/10.1007/s10796-016-9697-z
- Zhang, K., Xu, P., and Zhang, J. (2020). Explainable AI in deep reinforcement learning models: A SHAP method applied in power system emergency control. 2020 IEEE 4th Conference on Energy Internet and Energy System Integration: Connecting the Grids Towards a Low-Carbon High-Efficiency Energy System, EI2 2020, 711-716. http://doi.org/10.1109/TCSS.2021.3096824
- Zhang, L., Yan, Q., and Zhang, L. (2020). A text analytics framework for understanding the relationships among host self-description, trust perception and purchase behavior on Airbnb. Decision Support Systems, 133, 113288. https://doi.org/10.1016/j.dss.2020.113288
- Zhu, B., Zheng, X., Liu, H., Li, J., and Wang, P. (2020). Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics. Chaos, Solitons and Fractals, 140, 110123. https://doi.org/10.1016/j.chaos.2020.110123