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研究生: 康博元
Kang, Po-Yuan
論文名稱: 應用基於面向之情感分析方法探索影響消費者餐廳消費體驗之關鍵因素:以紐約市餐廳為例
Applying Aspect-based Sentiment Analysis to Explore the Key Factors Influencing Consumers' Restaurant Experience: An Example in New York City Restaurants
指導教授: 王維聰
Wang, Wei-Tsong
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理研究所
Institute of Information Management
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 77
中文關鍵詞: 餐廳消費者體驗基於面向的情感分析自然語言處理深度學習
外文關鍵詞: Restaurant Customer Experience, Aspect-based Sentiment Analysis, Natural Language Processing, Deep Learning
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  • 餐廳消費者的良好消費體驗,能提升消費者的再購意願與願付金額,為餐廳帶來獲利。影響餐廳消費體驗的面向多元且複雜,因此了解哪些面向對消費者體驗影響較大,以及餐廳在這些面向的表現如何,是餐廳管理者在改善企業表現時需要了解的重要問題。
    在過往文獻中,學者整理出影響餐廳消費體驗的四大關鍵因素,然而對更細微的因素的探討則較缺乏。針對上述問題,本研究應用基於面向的情感分析(Aspect-based sentiment analysis, ABSA)於餐廳評論進行分析,探索更細微的影響因素,以及餐廳在不同影響因素中的表現,並比較不同餐廳類型的結果。
    本研究蒐集Google Maps紐約市餐廳評論資料,並訓練深度學習模型進行ABSA。接著應用詞嵌入技術,將ABSA所獲得的面向字詞分群為20個群集。本研究將分群後獲得的群集作為次要面向進行命名與解釋,並進一步整合為五個主要面向。最後統計面向的出現頻率與各類情感出現頻率。
    本研究發現,在主要面向的層級,影響餐廳消費體驗的關鍵因素由重要性排序為食物、服務、內部環境、價值、外圍環境。在次要面向的層級,則發現消費者對於餐廳滿意與不滿意的影響因素不完全相同。本研究也應用語彙顯著性與價性分析方法(Lexicon salience and valence analysis, LSVA)分析餐廳面向的表現,給予餐廳管理上的建議,並發現消費者於不同類型的餐廳所重視的關鍵因素與該因素的滿意度皆有所不同。
    本研究在學術上補足了管理領域研究較少應用ABSA的缺口,提供了比四大關鍵因素更詳細的關鍵因素分析結果,並探討了不同餐廳類型之間關鍵因素的差異。於實務上本研究則提供餐廳管理人員評估餐廳表現的多個面向,並透過LSVA具體地提供餐廳可能的改進方向。

    A satisfying dining experience can greatly enhance a customer’s intention to repurchase and willingness to pay. Therefore, it is crucial for restaurants to have a comprehensive understanding of the aspects that significantly impact the customer experience and how they perform. To address this issue, this study applies aspect-based sentiment analysis (ABSA) to restaurant reviews to explore these factors and to compare the results across restaurant types.
    This study collected restaurant reviews from New York City on Google Maps and performed ABSA to derive aspect terms. The terms were then clustered into 20 secondary aspects using word embedding techniques and were further integrated into five primary aspects.
    The study found that the primary aspects affecting restaurant customer experience are food, service, internal environment, value, and external environment, in order of importance. At the level of secondary aspects, it was found that the factors that lead to customer satisfaction and dissatisfaction are not identical. This study also applied lexicon salience and valence analysis (LSVA) to analyze restaurant performance and provide management recommendations. The study discovered that the importance and satisfaction level of customers among the factors differs by types of restaurants.
    Academically, this study fills a gap in the management field by exploring the underutilization of ABSA, providing detailed key factor analysis results, and examining differences in key factors among restaurant types. In practical terms, this study provides aspects for restaurant managers to evaluate their performance and directions for improvement.

    摘要 i Extended Abstract ii 誌謝 vi 目錄 vii 表目錄 ix 圖目錄 x 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 2 第三節 研究範圍與限制 3 第四節 研究流程 3 第二章 文獻探討 5 第一節 餐廳消費體驗 5 第二節 餐廳網路評論 6 第三節 基於面向的情感分析 7 2.3.1 情感分析 7 2.3.2 基於面向的情感分析 8 第三章 研究方法 11 第一節 研究架構 11 第二節 資料蒐集與前處理 12 第三節 基於面向的情感分析 14 3.3.1 模型架構 14 3.3.2 BERT 16 3.3.3 資料格式轉換 17 3.3.4 局部上下文焦點機制(LCF) 18 3.3.5 模型訓練 19 第四節 面向整合 20 3.4.1 詞形還原 20 3.4.2 詞嵌入 21 第五節 結果分析 22 第四章 資料分析與結果 25 第一節 資料蒐集結果 25 第二節 模型訓練結果 26 第三節 面向整合結果 28 4.3.1 食物面向 28 4.3.2 服務面向 32 4.3.3 內部環境面向 35 4.3.4 價值面向 37 4.3.5 外圍環境面向 38 第四節 情感分析結果 39 4.4.1 主要面向 39 4.4.2 次要面向 40 第五節 LSVA結果與不同餐廳類型比較 42 4.5.1 主要面向 43 4.5.2 次要面向 46 第五章 結論 53 第一節 研究發現 53 第二節 研究貢獻 55 5.2.1 學術貢獻 55 5.2.2 實務貢獻 56 第三節 研究限制與未來研究方向 57 參考文獻 59

    Abalo, J., Varela, J., & Manzano, V. (2007). Importance values for Importance-Performance Analysis: A formula for spreading out values derived from preference rankings. Journal of Business Research, 60(2), 115-121.
    Abdullah, T., & Ahmet, A. (2023). Deep learning in sentiment analysis: Recent architectures. ACM Computing Surveys, 55(8), 1-37.
    Amoils, A. (2023). The top 10 wealthiest cities in the world in 2023. Henley & Partners. Retrieved Dec. 11, 2023 from https://www.henleyglobal.com/publications/wealthiest-cities/global-insights/top-10-wealthiest-cities-world-2023
    Bilgihan, A., Seo, S., & Choi, J. (2018). Identifying restaurant satisfiers and dissatisfiers: Suggestions from online reviews. Journal of Hospitality Marketing & Management, 27(5), 601-625.
    Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
    Chang, Y. C., Ku, C. H., & Nguyen, D. D. L. (2022). Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry. Information & Management, 59(2), 103587.
    Cheng, M. M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. International Journal of Hospitality Management, 76, 58-70.
    D'Aniello, G., Gaeta, M., & La Rocca, I. (2022). KnowMIS-ABSA: An overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis. Artificial Intelligence Review, 55(7), 5543-5574.
    Del Chiappa, G., Lorenzo-Romero, C., & Alarcon-del-Amo, M. D. (2018). Profiling tourists based on their perceptions of the trustworthiness of different types of peer-to-peer applications. Current Issues in Tourism, 21(3), 259-276.
    Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019, June). BERT: Pre-training of deep bidirectional transformers for language understanding. J. Burstein, C. Doran, & T. Solorio, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171-4186.
    Dixit, S., Badgaiyan, A. J., & Khare, A. (2019). An integrated model for predicting consumer's intention to write online reviews. Journal of Retailing and Consumer Services, 46, 112-120.
    Do, H. H., 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.
    Fellbaum, C. (2010). WordNet. In R. Poli, M. Healy, & A. Kameas (Eds.), Theory and Applications of Ontology: Computer Applications (pp. 231-243). Springer Netherlands. https://doi.org/10.1007/978-90-481-8847-5_10
    Fernández-Gavilanes, M., Juncal-Martínez, J., García-Méndez, S., Costa-Montenegro, E., & González-Castaño, F. J. (2018). Creating emoji lexica from unsupervised sentiment analysis of their descriptions. Expert Systems with Applications, 103, 74-91.
    GaWC. (2020). The world according to GaWC 2020. GaWC. Retrieved Dec. 11, 2023 from https://www.lboro.ac.uk/microsites/geography/gawc/world2020t.html
    Georgiadou, E., Angelopoulos, S., & Drake, H. (2020). Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes. International Journal of Information Management, 51, 102048.
    Godovykh, M., & Tasci, A. D. A. (2020). Customer experience in tourism: A review of definitions, components, and measurements. Tourism Management Perspectives, 35, 100694.
    Grégoire, Y., Ghadami, F., Laporte, S., Sénécal, S., & Larocque, D. (2018). How can firms stop customer revenge? The effects of direct and indirect revenge on post-complaint responses. Journal of the Academy of Marketing Science, 46(6), 1052-1071.
    Gupta, S., & Vajic, M. (2000). The contextual and dialectical nature of experiences. In New Service Development: Creating Memorable Experiences (pp. 33-51). SAGE Publications, Inc.
    Gursoy, D. (2019). A critical review of determinants of information search behavior and utilization of online reviews in decision making process (invited paper for 'luminaries' special issue of International Journal of Hospitality Management). International Journal of Hospitality Management, 76, 53-60.
    Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing & Management, 54(6), 1292-1307.
    He, P., Gao, J., & Chen, W. (2021). DeBERTaV3: Improving DeBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing. arXiv:2111.09543.
    Hickman, L., Thapa, S., Tay, L., Cao, M., & Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114-146.
    Jeong, B., Yoon, J., & Lee, J. M. (2019). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48, 280-290.
    Jivani, A. G. (2011). A comparative study of stemming algorithms. International Journal of Computer Applications in Technology, 2(6), 1930-1938.
    Kalnaovakul, K., & Promsivapallop, P. (2023). Hotel service quality dimensions and attributes: An analysis of online hotel customer reviews. Tourism and Hospitality Research, 23(3), 420-440.
    Kim, E. L., & Tanford, S. (2019). Simultaneous effects of multiple cues in restaurant reviews. Journal of Services Marketing, 33(5), 521-531.
    Kumar, V., Umashankar, N., Kim, K. H., & Bhagwat, Y. (2014). Assessing the Influence of Economic and Customer Experience Factors on Service Purchase Behaviors. Marketing Science, 33(5), 673-692.
    Kuppelwieser, V. G., Klaus, P., Manthiou, A., & Hollebeek, L. D. (2022). The role of customer experience in the perceived value-word-of-mouth relationship. Journal of Services Marketing, 36(3), 364-378.
    Li, H., Bruce, X. B., Li, G., & Gao, H. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 104707.
    Li, L. Y., Mao, Y. J., Wang, Y., & Ma, Z. H. (2022). How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis. Journal of Air Transport Management, 105, 102298.
    Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: A tertiary study [Review]. Artificial Intelligence Review, 54(7), 4997-5053.
    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), 3607-3633.
    Lynn, M. (2015). Service gratuities and tipping: A motivational framework. Journal of Economic Psychology, 46, 74-88.
    Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Haussler, T., Schmid-Petri, H., & Adam, S. (2018). Applying LDA topic modeling in communication research: Toward a valid and reliable methodology. Communication Methods and Measures, 12(2-3), 93-118.
    Manthiou, A., Hickman, E., & Klaus, P. (2020). Beyond good and bad: Challenging the suggested role of emotions in customer experience (CX) research. Journal of Retailing and Consumer Services, 57, 102218.
    Martilla, J. A., & James, J. C. (1977). Importance-Performance Analysis. Journal of Marketing, 41(1), 77-79.
    Mathayomchan, B., & Taecharungroj, V. (2020). “How was your meal?” Examining customer experience using Google maps reviews. International Journal of Hospitality Management, 90, 102641.
    Mehraliyev, F., Chan, I. C. C., & Kirilenko, A. P. (2022). Sentiment analysis in hospitality and tourism: A thematic and methodological review. International Journal of Contemporary Hospitality Management, 34(1), 46-77.
    Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116.
    Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv:1301.3781.
    Mir, J., Mahmood, A., & Khatoon, S. (2022). Multi-level knowledge engineering approach for mapping implicit aspects to explicit aspects. Computers, Materials and Continua, 70(2), 3491-3509.
    Nakayama, M., & Wan, Y. (2019). The cultural impact on social commerce: A sentiment analysis on Yelp ethnic restaurant reviews. Information & Management, 56(2), 271-279.
    Nazir, A., Rao, Y., Wu, L. W., & Sun, L. (2022). Issues and challenges of aspect-based sentiment analysis: A comprehensive survey. IEEE Transactions on Affective Computing, 13(2), 845-863.
    Paget, S. (2023). Local consumer review survey 2023. BrightLocal. Retrieved March 15, 2023 from https://www.brightlocal.com/research/local-consumer-review-survey/
    Pai, F.-Y., Yeh, T.-M., & Tang, C.-Y. (2018). Classifying restaurant service quality attributes by using Kano model and IPA approach. Total Quality Management & Business Excellence, 29(3-4), 301-328.
    Palmer, A. (2010). Customer experience management: A critical review of an emerging idea. Journal of Services Marketing, 24(3), 196-208.
    Pourfakhimi, S., Duncan, T., & Coetzee, W. J. L. (2020). Electronic word of mouth in tourism and hospitality consumer behaviour: State of the art. Tourism Review, 75(4), 637-661.
    Priyantina, R. A., & Sarno, R. (2019). Sentiment analysis of hotel reviews using Latent Dirichlet Allocation, semantic similarity and LSTM. International Journal of Intelligent Engineering and Systems, 12(4), 142-155.
    ReviewTrackers. (2022). Online reviews statistics and trends: A 2022 report by ReviewTrackers. ReviewTrackers. Retrieved March 15, 2023 from https://www.reviewtrackers.com/reports/online-reviews-survey/
    Ricard, B. J., Marsch, L. A., Crosier, B., & Hassanpour, S. (2018). Exploring the utility of community-generated social media content for detecting depression: An analytical study on Instagram. Journal of Medical Internet Research, 20(12), e11817.
    Rogers, A., Kovaleva, O., & Rumshisky, A. (2020). A primer in BERTology: What we know about how BERT works. Transactions of the Association for Computational Linguistics, 8, 842-866.
    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.
    SuperGLUE. (n.d.). Leaderboard version: 2.0. SuperGLUE. Retrieved May 17, 2023 from https://super.gluebenchmark.com/leaderboard/
    Taecharungroj, V., & Mathayomchan, B. (2019). Analysing TripAdvisor reviews of tourist attractions in Phuket, Thailand. Tourism Management, 75, 550-568.
    Thomas, M. J., Wirtz, B. W., & Weyerer, J. C. (2019). Determinants of online review credibility and its impact on consumers' purchase intention. Journal of Electronic Commerce Research, 20(1), 1-20.
    Tian, G., Lu, L., & McIntosh, C. (2021). What factors affect consumers' dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, 104060.
    U.S. Census Bureau. (n.d.). U.S. Census Bureau QuickFacts: New York City, New York. U.S. Census Bureau. Retrieved Dec. 11, 2023 from https://www.census.gov/quickfacts/fact/table/newyorkcitynewyork/LFE041222#LFE041222
    Ukpabi, D. C., & Karjaluoto, H. (2018). What drives travelers' adoption of user-generated content? A literature review. Tourism Management Perspectives, 28, 251-273.
    United Nations. (2023). 2022 demographic yearbook. United Nations.
    Vu, H. Q., Li, G., Law, R., & Zhang, Y. C. (2019). Exploring tourist dining preferences based on restaurant reviews. Journal of Travel Research, 58(1), 149-167.
    Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2019). SuperGLUE: A stickier benchmark for general-purpose language understanding systems. In Proceedings of the 33rd International Conference on Neural Information Processing Systems (pp. 3266–3280). Curran Associates Inc.
    Wang, Y. Q., & Kim, J. (2021). Interconnectedness between online review valence, brand, and restaurant performance. Journal of Hospitality and Tourism Management, 48, 138-145.
    Yang, H., Zeng, B. Q., Yang, J. H., Song, Y. W., & Xu, R. Y. (2021). A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction. Neurocomputing, 419, 344-356.
    Yang, H., Zhang, C., & Li, K. (2023). PyABSA: A modularized framework for reproducible aspect-based sentiment analysis. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 5117–5122). Association for Computing Machinery.
    Yrjola, M., Rintamaki, T., Saarijarvi, H., Joensuu, J., & Kulkarni, G. (2019). A customer value perspective to service experiences in restaurants. Journal of Retailing and Consumer Services, 51, 91-101.
    Zeng, B. Q., Yang, H., Xu, R. Y., Zhou, W., & Han, X. L. (2019). LCF: A local context focus mechanism for aspect-based sentiment classification. Applied Sciences, 9(16), 3389.
    Zhang, L., & Hanks, L. (2018). Online reviews: The effect of cosmopolitanism, incidental similarity, and dispersion on consumer attitudes toward ethnic restaurants. International Journal of Hospitality Management, 68, 115-123.
    Zhou, J., Huang, J. X., Chen, Q., Hu, Q. V., Wang, T. T., & He, L. (2019). Deep learning for aspect-level sentiment classification: Survey, vision, and challenges. IEEE Access, 7, 78454-78483.
    Zhu, B., Guo, D. F., & Ren, L. (2022). Consumer preference analysis based on text comments and ratings: A multi-attribute decision-making perspective. Information & Management, 59(3), 103626.

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