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題名 基於使用者留言情緒分析之TRIZ管理策略提升圖書館服務滿意度
A TRIZ Management Strategy based on Sentiment Analysis of Users’ Comments to Improve Library Service Satisfaction作者 劉家欣
Liu, Chia-Hsin貢獻者 陳志銘
Chen, Chih-Ming
劉家欣
Liu, Chia-Hsin關鍵詞 圖書館服務滿意度
社群聆聽
自然語言處理
情感分析
TRIZ工具
Library Satisfaction
Natural Language Processing
Sentiment Analysis
Social Listening
TRIZ日期 2025 上傳時間 4-Feb-2025 16:22:10 (UTC+8) 摘要 本研究旨在採用自然語言處理技術探討如何利用使用者在 Google Maps 上針對三間國立圖書館的留言評論，透過自然語言處理技術分析使用者情緒，將其區分為正面和負面評價之評論，並量化其權重，再運用 TRIZ 理論找出改善圖書館服務的創新管理策略，以提升使用者滿意度。其研究過程包含資料收集、情緒分析、主題分析，以及 TRIZ 方法的應用，並針對研究結果提出具體的提升服務滿意度改善建議，以及未來的研究方向。 本研究分析了三間圖書館（國立臺灣圖書館、國立公共資訊圖書館、國家圖書館）的評論，研究結果發現，使用者對於圖書館的滿意度與環境舒適度、藏書豐富度，以及設施便利性等面向相關。特別是三間圖書館所面臨的共同問題包括座位不足、空間擁擠、噪音等。其中針對座位不足的問題，本研究提出統一座位規格、彈性座位區、多功能家俱、利用閒置時段、移動式座位、多元座位等解決方案。此外，針對噪音問題，本研究提出設立快速反應團隊、將噪音源轉化為提醒訊號、建立可移動的隔音屏障、提供預防噪音的工具等解決方案。 本研究提出應用科技與創新方法協助圖書館積極重視使用者感受，不斷優化服務與管理，並建議圖書館可以透過提升館員服務態度、改善預約系統、定期更新資源、加強環境清潔等來滿足使用者期望。
This study aims to explore how to utilize user comments on Google Maps regarding three national libraries by employing natural language processing techniques. It analyzes user sentiments through natural language processing, categorizing them into positive and negative evaluations, quantifying their weights, and then applying TRIZ theory to identify innovative management strategies for improving library services to enhance user satisfaction. The research process includes data collection, sentiment analysis, thematic analysis, and the application of TRIZ methods. It also provides specific recommendations for improving service satisfaction based on the research results and outlines future research directions. The study analyzes comments from three libraries (National Taiwan Library, National Library of Public Information, and National Central Library). The findings indicate that user satisfaction with the libraries is related to factors such as environmental comfort, richness of collections, and convenience of facilities. Notably, common issues faced by the three libraries include insufficient seating, crowded spaces, and noise. In addressing the issue of insufficient seating, this study proposes solutions such as standardizing seating specifications, creating flexible seating areas, using multifunctional furniture, utilizing idle time slots, and incorporating mobile and diverse seating options. Additionally, regarding the noise issue, this study suggests establishing rapid response teams, transforming noise sources into alert signals, creating movable soundproof barriers, and providing tools to prevent noise. This study apply technology and innovative methods that libraries should pay attention to user feelings, continuously optimize services and management, and recommends that libraries can meet user expectations by enhancing staff service attitudes, improving reservation systems, regularly updating resources, and strengthening environmental cleanliness.參考文獻 英文文獻 Ali, A. A. S., & Shandilya, V. K. (2021). AI-Natural Language Processing (NLP). International Journal for Research in Applied Science and Engineering Technology, 9, 135-140. Alm, C. (2005). Emotions from text: machine learning for text-based emotion prediction. In Proceedings of Joint Conference on HLT/EMNLP. Alʹtshuller, G. S. (1996).And suddenly the inventor appeared: TRIZ, the theory of inventive problem solving. Technical Innovation Center, Inc.. Borrego, Á., & Comalat Navarra, M. (2021). What users say about public libraries: an analysis of Google Maps reviews. Online Information Review, 45(1), 84-98 Busacca, B., & Padula, G. (2005). Understanding the relationship between attribute performance and overall satisfaction: Theory, measurement and implications. Marketing Intelligence & Planning, 23(6), 543-561. Chen, C. M., Ho, S. Y., & Chang, C. (2023). A hierarchical topic analysis tool to facilitate digital humanities research. Aslib Journal of Information Management, 75(1), 1-19. Cardozo,R.N.(1965).An experimental study of customer effort, expectation, and satisfaction.Journal of marketing research,2(3), 244-249. Cascini, G. (2012). TRIZ-based anticipatory design of future products and processes. Journal of Integrated Design and Process Science, 16(3), 29-63. Chaffey, D., & Smith, P. R. (2022). Digital marketing excellence: planning, optimizing and integrating online marketing. Routledge. Chen, C. C., & Chang, C. C. (2024). Evaluating Public Library Services Through User-Generated Content: Social Network Analysis of Google Maps Reviews. Chen, M. C., Chiu, A. L., & Chang, H. H. (2005). Mining changes in customer behavior in retail marketing.Expert Systems with Applications,28(4), 773-781.. Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649. Curran, T., Treiber, J., & Rosenblatt, M. (2018). Building brands through social listening. Proceedings of the Northeast Business & Economics Association, 74-77. Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2020). Sentiment analysis based on deep learning: A comparative study. Electronics, 9(3), 483. Gaha, U., & Hall, S. (2015). Sustainable use of social media in libraries. Codex: the Journal of the Louisiana Chapter of the ACRL, 3(2), 47-67. Gronauer, B., & Naehler, H. (2016). TRIZ as an amplifier for corporate creativity and corporate innovation ability. Procedia CIRP,39, 185-190. Hasan, M. R., Maliha, M., & Arifuzzaman, M. (2019, July). Sentiment analysis with NLP on Twitter data. In 2019 international conference on computer, communication, chemical, materials and electronic engineering (IC4ME2) (pp. 1-4). Hua, Z., Yang, J., Coulibaly, S., & Zhang, B. (2006). Integration TRIZ with problem-solving tools: a literature review from 1995 to 2006. International journal of business innovation and research,1(1-2), 111-128. Ilevbare, I. M., Probert, D., & Phaal, R. (2013). A review of TRIZ, and its benefits and challenges in practice. Technovation, 33(2-3), 30-37. Kassim, N. A. (2009). EVALUATING USERS’SATISFACTION ON ACADEMIC LIBRARY PERFORMANCE.Malaysian journal of library and information science, 14(2), 101-115. Khan, A. M., & Loan, F. A. (2022). Exploring the reviews of Google Maps to assess the user opinions about public libraries. Library Management, 43(8-9), 601-615. Khan, M. T., Durrani, M., Ali, A., Inayat, I., Khalid, S., & Khan, K. H. (2016). Sentiment analysis and the complex natural language. Complex Adaptive Systems Modeling, 4, 1-19. Mann, D. (2001). Systematic win–win problem solving in a business environment. growth, 88, 95. Mattews,J.R.(2008). Customer Satisfaction. Public Libraries, 47(6),52-55. Meredith Corporation Case Study Google Cloud. (n.d.). https://cloud.google.com/customers/meredith Phuangsuwan, P., Siripipatthanakul, S., Limna, P., & Pariwongkhuntorn, N. (2024). The Impact of Google Maps Application on the Digital Economy. Phuangsuwan, P., Siripipatthanakul, S., Limna, P., & Pariwongkhuntorn, (2024), 192-203. Pokhrel, C. (2013). Adaptation of TRIZ Method for Problem Solving in Process Engineering. Pokhrel, C., Cruz, C., Ramirez, Y., & Kraslawski, A. (2015). Adaptation of TRIZ contradiction matrix for solving problems in process engineering. Chemical engineering research and design, 103, 3-10. Rantanen, K., Conley, D. W., & Domb, E. R. (2017). Simplified TRIZ: New problem solving applications for technical and business professionals. Taylor & Francis. Ruchti, B., & Livotov, P. (2001). TRIZ-based innovation principles and a process for problem solving in business and management. The TRIZ Journal, 1(2001), 677-687. Spreafico, C., & Russo, D. (2016). TRIZ industrial case studies: a critical survey. Procedia Cirp, 39, 51-56. Stewart, M. C., & Arnold, C. L. (2018). Defining social listening: Recognizing an emerging dimension of listening.International journal of listening,32(2), 85-100. Stewart, M. C., Atilano, M., & Arnold, C. L. (2018). Improving customer relationship management through social listening: A case study of an American Academic Library. In Diverse methods in customer relationship marketing and management (pp. 202-222). IGI Global. Souchkov, V. (2010). TRIZ and systematic business model innovation. In Global ETRIA Conference ‘TRIZ Future (pp. 3-5). Taj, M. N., & Girisha, G. S. (2021). Insights of strength and weakness of evolving methodologies of sentiment analysis. Global Transitions Proceedings, 2(2), 157-162. Teplov, R., Chechurin, L., & Podmetina, D. (2017). TRIZ as innovation management tool: Insights from academic literature. International Journal of Technology Marketing, 12(3), 207-229. Tripathi, R. (2021). Use of web analytics and social listening to attract international students. In Global Perspectives on Recruiting International Students: Challenges and Opportunities (pp. 65-79). Emerald Publishing Limited. Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media.Knowledge and Information Systems,60, 617-663. 中文文獻 王晨安. (2017). 基於中文情緒構面模型之電影評論意見分析.清華大學資訊系統與應用研究所學位論文,2017, 1-55. 王麗蕉, & 鄭雅靜. (2006). 大學圖書館評鑑之探討. 國家圖書館館刊,九十五年(第一期), 35–58. 吳政諺, & 張哲偉. (2017). 社群聆聽, 聽什麼?.會計研究月刊, (384), 104-108. 林永禎(2023) .商業管理萃思(TRIZ)理論與實務:讓你發明新的服務。臺北市：五南。 林宜瑩(2023) .以TRIZ 理論探究居家長照機構於營運上的新創管理與永續經營之關係. 林素芳(2023) .應用TRIZ 方法建構補習班口碑行銷策略之研究. 洪慧珊. (2002). 校園開放空間使用者滿意度研究-以東華大學校園核心區為例.國立東華大學自然資源管理研究所碩士論文,花蓮, 1-13. 孫子緹. (2022). 應用TRIZ理論分析管理顧問業創新商業生態系統之研究-以普羅加斯為例. 張淑惠. (1994). 圖書館績效評估之研究. 郭俊桔, & 張育蓉. (2013). 使用情緒分析於圖書館使用者滿意度評估之研究 彭金堂, 鄭姿均, 卓欣姿, 陳幸慈, & 程瑞琪. (2010). 圖書館服務品質滿意度之研究-以某科技大學圖書館為例.績效與策略研究,7(1), 53-73. 楊立偉. (2020).社群大數據：網路聲量、口碑及輿情分析(第二版). 謝寶煖. (1998). 從顧客觀點來談圖書館的績效評估. 國立成功大學圖書館館刊,1, 10–20. 描述 碩士
國立政治大學
圖書資訊學數位碩士在職專班
107913003資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107913003 資料類型 thesis dc.contributor.advisor 陳志銘 zh_TW dc.contributor.advisor Chen, Chih-Ming en_US dc.contributor.author (Authors) 劉家欣 zh_TW dc.contributor.author (Authors) Liu, Chia-Hsin en_US dc.creator (作者) 劉家欣 zh_TW dc.creator (作者) Liu, Chia-Hsin en_US dc.date (日期) 2025 en_US dc.date.accessioned 4-Feb-2025 16:22:10 (UTC+8) - dc.date.available 4-Feb-2025 16:22:10 (UTC+8) - dc.date.issued (上傳時間) 4-Feb-2025 16:22:10 (UTC+8) - dc.identifier (Other Identifiers) G0107913003 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/155533 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 圖書資訊學數位碩士在職專班 zh_TW dc.description (描述) 107913003 zh_TW dc.description.abstract (摘要) 本研究旨在採用自然語言處理技術探討如何利用使用者在 Google Maps 上針對三間國立圖書館的留言評論，透過自然語言處理技術分析使用者情緒，將其區分為正面和負面評價之評論，並量化其權重，再運用 TRIZ 理論找出改善圖書館服務的創新管理策略，以提升使用者滿意度。其研究過程包含資料收集、情緒分析、主題分析，以及 TRIZ 方法的應用，並針對研究結果提出具體的提升服務滿意度改善建議，以及未來的研究方向。 本研究分析了三間圖書館（國立臺灣圖書館、國立公共資訊圖書館、國家圖書館）的評論，研究結果發現，使用者對於圖書館的滿意度與環境舒適度、藏書豐富度，以及設施便利性等面向相關。特別是三間圖書館所面臨的共同問題包括座位不足、空間擁擠、噪音等。其中針對座位不足的問題，本研究提出統一座位規格、彈性座位區、多功能家俱、利用閒置時段、移動式座位、多元座位等解決方案。此外，針對噪音問題，本研究提出設立快速反應團隊、將噪音源轉化為提醒訊號、建立可移動的隔音屏障、提供預防噪音的工具等解決方案。 本研究提出應用科技與創新方法協助圖書館積極重視使用者感受，不斷優化服務與管理，並建議圖書館可以透過提升館員服務態度、改善預約系統、定期更新資源、加強環境清潔等來滿足使用者期望。 zh_TW dc.description.abstract (摘要) This study aims to explore how to utilize user comments on Google Maps regarding three national libraries by employing natural language processing techniques. It analyzes user sentiments through natural language processing, categorizing them into positive and negative evaluations, quantifying their weights, and then applying TRIZ theory to identify innovative management strategies for improving library services to enhance user satisfaction. The research process includes data collection, sentiment analysis, thematic analysis, and the application of TRIZ methods. It also provides specific recommendations for improving service satisfaction based on the research results and outlines future research directions. The study analyzes comments from three libraries (National Taiwan Library, National Library of Public Information, and National Central Library). The findings indicate that user satisfaction with the libraries is related to factors such as environmental comfort, richness of collections, and convenience of facilities. Notably, common issues faced by the three libraries include insufficient seating, crowded spaces, and noise. In addressing the issue of insufficient seating, this study proposes solutions such as standardizing seating specifications, creating flexible seating areas, using multifunctional furniture, utilizing idle time slots, and incorporating mobile and diverse seating options. Additionally, regarding the noise issue, this study suggests establishing rapid response teams, transforming noise sources into alert signals, creating movable soundproof barriers, and providing tools to prevent noise. This study apply technology and innovative methods that libraries should pay attention to user feelings, continuously optimize services and management, and recommends that libraries can meet user expectations by enhancing staff service attitudes, improving reservation systems, regularly updating resources, and strengthening environmental cleanliness. en_US dc.description.tableofcontents 第一章 緒論 9 第一節 研究背景與動機 9 第二節 研究目的與問題 10 第三節 研究範圍與限制 11 第四節 重要名詞解釋 12 第二章 文獻探討 14 第一節 自然語言處理與情緒分析 14 第二節 社群聆聽 15 第三節 圖書館服務的滿意度 16 第四節 TRIZ 18 第三章 研究方法 20 第一節 研究架構 20 第二節 研究流程 21 第三節 研究對象 25 第四節 研究設計 26 第四章 研究結果 46 第一節 Google - Natural Language AI分析結果 46 第二節 主題分析結果 53 第三節 TRIZ管理策略 61 第四節 綜合討論 69 第五章 結論與建議 72 第一節 結論 72 第二節 建議 74 第三節 未來研究方向 75 英文文獻 77 中文文獻 80 附錄 81 附錄一 TRIZ 31項商業管理參數 81 附錄二 TRIZ 40項商業管理創新發明原則 83 附錄三 矛盾矩陣表 85 zh_TW dc.format.extent 4693407 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107913003 en_US dc.subject (關鍵詞) 圖書館服務滿意度 zh_TW dc.subject (關鍵詞) 社群聆聽 zh_TW dc.subject (關鍵詞) 自然語言處理 zh_TW dc.subject (關鍵詞) 情感分析 zh_TW dc.subject (關鍵詞) TRIZ工具 zh_TW dc.subject (關鍵詞) Library Satisfaction en_US dc.subject (關鍵詞) Natural Language Processing en_US dc.subject (關鍵詞) Sentiment Analysis en_US dc.subject (關鍵詞) Social Listening en_US dc.subject (關鍵詞) TRIZ en_US dc.title (題名) 基於使用者留言情緒分析之TRIZ管理策略提升圖書館服務滿意度 zh_TW dc.title (題名) A TRIZ Management Strategy based on Sentiment Analysis of Users’ Comments to Improve Library Service Satisfaction en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 英文文獻 Ali, A. A. S., & Shandilya, V. K. (2021). AI-Natural Language Processing (NLP). International Journal for Research in Applied Science and Engineering Technology, 9, 135-140. Alm, C. (2005). Emotions from text: machine learning for text-based emotion prediction. In Proceedings of Joint Conference on HLT/EMNLP. Alʹtshuller, G. S. (1996).And suddenly the inventor appeared: TRIZ, the theory of inventive problem solving. Technical Innovation Center, Inc.. Borrego, Á., & Comalat Navarra, M. (2021). What users say about public libraries: an analysis of Google Maps reviews. Online Information Review, 45(1), 84-98 Busacca, B., & Padula, G. (2005). Understanding the relationship between attribute performance and overall satisfaction: Theory, measurement and implications. Marketing Intelligence & Planning, 23(6), 543-561. Chen, C. M., Ho, S. Y., & Chang, C. (2023). A hierarchical topic analysis tool to facilitate digital humanities research. Aslib Journal of Information Management, 75(1), 1-19. Cardozo,R.N.(1965).An experimental study of customer effort, expectation, and satisfaction.Journal of marketing research,2(3), 244-249. Cascini, G. (2012). TRIZ-based anticipatory design of future products and processes. Journal of Integrated Design and Process Science, 16(3), 29-63. Chaffey, D., & Smith, P. R. (2022). Digital marketing excellence: planning, optimizing and integrating online marketing. Routledge. Chen, C. C., & Chang, C. C. (2024). Evaluating Public Library Services Through User-Generated Content: Social Network Analysis of Google Maps Reviews. Chen, M. C., Chiu, A. L., & Chang, H. H. (2005). Mining changes in customer behavior in retail marketing.Expert Systems with Applications,28(4), 773-781.. Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649. Curran, T., Treiber, J., & Rosenblatt, M. (2018). Building brands through social listening. Proceedings of the Northeast Business & Economics Association, 74-77. Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2020). Sentiment analysis based on deep learning: A comparative study. Electronics, 9(3), 483. Gaha, U., & Hall, S. (2015). Sustainable use of social media in libraries. Codex: the Journal of the Louisiana Chapter of the ACRL, 3(2), 47-67. Gronauer, B., & Naehler, H. (2016). TRIZ as an amplifier for corporate creativity and corporate innovation ability. Procedia CIRP,39, 185-190. Hasan, M. R., Maliha, M., & Arifuzzaman, M. (2019, July). Sentiment analysis with NLP on Twitter data. In 2019 international conference on computer, communication, chemical, materials and electronic engineering (IC4ME2) (pp. 1-4). Hua, Z., Yang, J., Coulibaly, S., & Zhang, B. (2006). Integration TRIZ with problem-solving tools: a literature review from 1995 to 2006. International journal of business innovation and research,1(1-2), 111-128. Ilevbare, I. M., Probert, D., & Phaal, R. (2013). A review of TRIZ, and its benefits and challenges in practice. Technovation, 33(2-3), 30-37. Kassim, N. A. (2009). EVALUATING USERS’SATISFACTION ON ACADEMIC LIBRARY PERFORMANCE.Malaysian journal of library and information science, 14(2), 101-115. Khan, A. M., & Loan, F. A. (2022). Exploring the reviews of Google Maps to assess the user opinions about public libraries. Library Management, 43(8-9), 601-615. Khan, M. T., Durrani, M., Ali, A., Inayat, I., Khalid, S., & Khan, K. H. (2016). Sentiment analysis and the complex natural language. Complex Adaptive Systems Modeling, 4, 1-19. Mann, D. (2001). Systematic win–win problem solving in a business environment. growth, 88, 95. Mattews,J.R.(2008). Customer Satisfaction. Public Libraries, 47(6),52-55. Meredith Corporation Case Study Google Cloud. (n.d.). https://cloud.google.com/customers/meredith Phuangsuwan, P., Siripipatthanakul, S., Limna, P., & Pariwongkhuntorn, N. (2024). The Impact of Google Maps Application on the Digital Economy. Phuangsuwan, P., Siripipatthanakul, S., Limna, P., & Pariwongkhuntorn, (2024), 192-203. Pokhrel, C. (2013). Adaptation of TRIZ Method for Problem Solving in Process Engineering. Pokhrel, C., Cruz, C., Ramirez, Y., & Kraslawski, A. (2015). Adaptation of TRIZ contradiction matrix for solving problems in process engineering. Chemical engineering research and design, 103, 3-10. Rantanen, K., Conley, D. W., & Domb, E. R. (2017). Simplified TRIZ: New problem solving applications for technical and business professionals. Taylor & Francis. Ruchti, B., & Livotov, P. (2001). TRIZ-based innovation principles and a process for problem solving in business and management. The TRIZ Journal, 1(2001), 677-687. Spreafico, C., & Russo, D. (2016). TRIZ industrial case studies: a critical survey. Procedia Cirp, 39, 51-56. Stewart, M. C., & Arnold, C. L. (2018). Defining social listening: Recognizing an emerging dimension of listening.International journal of listening,32(2), 85-100. Stewart, M. C., Atilano, M., & Arnold, C. L. (2018). Improving customer relationship management through social listening: A case study of an American Academic Library. In Diverse methods in customer relationship marketing and management (pp. 202-222). IGI Global. Souchkov, V. (2010). TRIZ and systematic business model innovation. In Global ETRIA Conference ‘TRIZ Future (pp. 3-5). Taj, M. N., & Girisha, G. S. (2021). Insights of strength and weakness of evolving methodologies of sentiment analysis. Global Transitions Proceedings, 2(2), 157-162. Teplov, R., Chechurin, L., & Podmetina, D. (2017). TRIZ as innovation management tool: Insights from academic literature. International Journal of Technology Marketing, 12(3), 207-229. Tripathi, R. (2021). Use of web analytics and social listening to attract international students. 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