隨著數位科技迅速演進與人工智慧應用普及，企業愈加重視運用顧客回饋資料，以提升產品與服務的整體價值體驗。傳統方法多依賴人工分析評論與市場趨勢，不僅耗時低效，亦難以及時掌握需求變化與隱含意圖。
為回應此挑戰，本研究開發一套整合大型語言模型（Large Language Model, LLM）、語意檢索（Semantic Retrieval）及圖譜推理的顧客價值體驗優化代理人（Customer Value Optimization Agent），涵蓋四大核心面向：(1) 資料收集與處理：建立評論資料的擷取、清理與標準化流程，確保後續分析的品質與一致性；(2) 評論內容分析：運用評論切分與構面情感分析（Aspect-Based Sentiment Analysis, ABSA）萃取主題、對象、構面與情感等結構化資訊；(3) 知識圖譜建構：建立實體與關聯節點，結合嵌入式資料庫生成語意索引，並開發維護與動態擴充機制，支援多層級查詢與即時更新；(4) 顧客價值優化：整合跨構面的語意資訊，結合檢索增強生成（Retrieval-Augmented Generation, RAG）生成策略性市場分析與回應建議。
實驗結果顯示，代理人在多項任務中表現優異：評論切分 BERTScore-F1 為 0.9409，主題分類準確率 97.6%，構面識別 F1 0.7949，意見詞匹配 F1 0.9461，情感預測 MAE 0.1115。在 RAG 回應任務中，本研究設計之流程於三類市場問題中共取得 44 次勝出，特別於「全面性」、「多樣性」及「賦能性」三構面具顯著優勢，展現結合語意理解與結構推理的生成能力。
綜上，本研究驗證了結合 LLM 與知識圖譜於顧客價值體驗優化的可行性與有效性，並為智慧型顧客決策支援系統的發展提供關鍵技術與實作依據。

With the rapid growth of digital technologies and AI applications, enterprises increasingly leverage customer feedback to enhance product and service value. Traditional manual review analysis is time-consuming, inefficient, and unable to promptly detect evolving needs.
This study proposes a Customer Value Optimization Agent integrating Large Language Models (LLMs), semantic retrieval, and knowledge graph reasoning. It includes: (1) Data processing – extracting, cleaning, and standardizing reviews; (2) Content analysis – review segmentation and Aspect-Based Sentiment Analysis (ABSA) to extract topics, entities, aspects, and sentiments; (3) Knowledge graph construction – semantic indexing for multi-level queries and real-time updates; (4) Value optimization – cross-aspect integration with Retrieval-Augmented Generation (RAG) for strategic market insights.
Experiments achieved BERTScore-F1 0.9409 for segmentation, 97.6% topic accuracy, F1 scores of 0.7949 for aspect detection and 0.9461 for opinion matching, and sentiment MAE 0.1115. In RAG tasks, the method outperformed the baseline in 44 of 60 queries, excelling in comprehensiveness, diversity, and empowerment.
This confirms the feasibility of combining LLMs and knowledge graphs for customer value optimization, offering a foundation for intelligent decision support systems.

陳俊諺. (2024). 以Roberta-BiLSTM-CNN及決策樹為基之顧客價值體驗優化方法與技術研究. PhD Thesis.
Biswas, B., Sengupta, P., Kumar, A., Delen, D., & Gupta, S. (2022). A critical assessment of consumer reviews: A hybrid NLP-based methodology. Decision Support Systems, 159, 113799. https://doi.org/10.1016/j.dss.2022.113799
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., . . . Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2005.14165
Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/mis.2013.30
De Caigny, A., Coussement, K., Hoornaert, S., & Meire, M. (2025). Life event-based marketing using AI. Journal of Business Research, 193, 115349. https://doi.org/10.1016/j.jbusres.2025.115349
Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLORA: Efficient Finetuning of Quantized LLMS. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2305.14314
Du, P. (2025). OmniNova:A General Multimodal Agent Framework. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2503.20028
Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., & Larson, J. (2024). From Local to Global: A Graph RAG Approach to Query-Focused Summarization. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2404.16130
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., & Wang, H. (2023). Retrieval-Augmented Generation for Large Language Models: A survey. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2312.10997
Gelashvili, V., Martínez-Navalón, J. G., DeMatos, N., & De Brito Correia, M. (2024). Technological transformation: The importance of E-WOM and perceived privacy in the context of opinion platforms. Technological Forecasting and Social Change, 205, 123472. https://doi.org/10.1016/j.techfore.2024.123472
Gentile, C., Spiller, N., & Noci, G. (2007). How to sustain the customer experience:: An overview of experience components that co-create value with the customer. European management journal, 25(5), 395-410. Guo, Z., Xia, L., Yu, Y., Ao, T., & Huang, C. (2024). LightRAG: Simple and Fast Retrieval-Augmented Generation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2410.05779
Ghisellini, R., Pareschi, R., Pedroni, M., & Raggi, G. B. (2025a). Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics. Information, 16(3), 192. https://doi.org/10.3390/info16030192
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38–52. https://doi.org/10.1002/dir.10073
Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., & Wu, C. (2023). MetaGPT: Meta Programming for a Multi-Agent Collaborative Framework. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2308.00352
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., & Chen, W. (2021). LORA: Low-Rank adaptation of Large Language Models. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2106.09685
Jiang, R., Chen, K., Bai, X., He, Z., Li, J., Yang, M., Zhao, T., Nie, L., & Zhang, M. (2024). A survey on Human preference learning for large language models. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.11191
Kuan-Chieh, Wang, Ostashev, D., Fang, Y., Tulyakov, S., & Aberman, K. (2024). MOA: Mixture-of-Attention for Subject-Context disentanglement in Personalized Image Generation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2404.11565
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how? International Journal of Information Management, 77, 102783. https://doi.org/10.1016/j.ijinfomgt.2024.102783
Lammel, B., Korkut, S., & Hinkelmann, K. (2016, December). Customer experience modelling and analysis framework. In Proceedings of the Annual International Conference on Innovation and Entrepreneurship (IE 2016) (Vol. 24). https://doi.org/10.5176/2251-2039_IE16.10
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
Lewis, P. S. H., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP tasks. Neural Information Processing Systems, 33, 9459–9474. https://proceedings.neurips.cc/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf
Li, W., Lin, Y., Xia, M., & Jin, C. (2025). Rethinking Mixture-of-Agents: Is mixing different large language models beneficial? arXiv (Cornell University). https://doi.org/10.48550/arxiv.2502.00674
Liu, H., Shaalan, A., & Jayawardhena, C. (2022). The impact of Electronic Word-of-Mouth (EWOM) on consumer behaviours. In SAGE Publications Ltd eBooks (pp. 136–158). https://doi.org/10.4135/9781529782509.n9
Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504. https://doi.org/10.1016/j.ijresmar.2020.04.005
Ma, L., Tang, S., Ganesh, A., Chen, J., Padmanabhan, A., Patel, M., Xu, J., Cho, J., Korpeoglu, E., Kumar, S., & Achan, K. (2025). ROSI: A hybrid solution for omni-channel feature integration in E-commerce. Data & Knowledge Engineering, 102465. https://doi.org/10.1016/j.datak.2025.102465
Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., & Gao, J. (2024). Large Language Models: a survey. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2402.06196
Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 34(1), 185-200.
Nazir, A., Rao, Y., Wu, L., & Sun, L. (2020). Issues and Challenges of Aspect-based Sentiment Analysis: A Comprehensive survey. IEEE Transactions on Affective Computing, 13(2), 845–863. https://doi.org/10.1109/taffc.2020.2970399
Ngai, E., Xiu, L., & Chau, D. (2008). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems With Applications, 36(2), 2592–2602. https://doi.org/10.1016/j.eswa.2008.02.021
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2203.02155
Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167–176. https://doi.org/10.1509/jmkg.2005.69.4.167
Peng, C., Xia, F., Naseriparsa, M., & Osborne, F. (2023). Knowledge Graphs: Opportunities and challenges. Artificial Intelligence Review, 56(11), 13071–13102. https://doi.org/10.1007/s10462-023-10465-9
Prahalad, C., & Ramaswamy, V. (2004). Co-creation experiences: The next practice in value creation. Journal of Interactive Marketing, 18(3), 5–14. https://doi.org/10.1002/dir.20015
Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023). Direct Preference Optimization: Your Language Model is Secretly a Reward Model. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2305.18290
Rashid, M. R. A., Hasan, K. F., Hasan, R., Das, A., Sultana, M., & Hasan, M. (2024). A comprehensive dataset for sentiment and emotion classification from Bangladesh e-commerce reviews. Data in Brief, 53, 110052. https://doi.org/10.1016/j.dib.2024.110052
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Alpaca: A strong, replicable instruction-following modely, March 2023.
Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. K. (2024). LLMs in e-commerce: A comparative analysis of GPT and LLaMA models in product review evaluation. Natural Language Processing Journal, 6, 100056. https://doi.org/10.1016/j.nlp.2024.100056
Roy, S. K., Tehrani, A. N., Pandit, A., Apostolidis, C., & Ray, S. (2025). Ai-capable relationship marketing: Shaping the future of customer relationships. Journal of Business Research, 192, 115309. https://doi.org/10.1016/j.jbusres.2025.115309
Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1249
Saha, V., Mani, V., & Goyal, P. (2020). Emerging trends in the literature of value co-creation: a bibliometric analysis. Benchmarking an International Journal, 27(3), 981–1002. https://doi.org/10.1108/bij-07-2019-0342
Sequeda, J., Allemang, D., & Jacob, B. (2025). Knowledge Graphs as a source of trust for LLM-powered enterprise question answering. Journal of Web Semantics, 100858. https://doi.org/10.1016/j.websem.2024.100858
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D. M., Lowe, R., Voss, C., Radford, A., Amodei, D., & Christiano, P. (2020). Learning to summarize from human feedback. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2009.01325
Taneja, K., Vashishtha, J., & Ratnoo, S. (2024). Transformer based Unsupervised learning approach for imbalanced text sentiment analysis of E-Commerce reviews. Procedia Computer Science, 235, 2318–2331. https://doi.org/10.1016/j.procs.2024.04.220
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2019). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer Experience Creation: determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31–41. https://doi.org/10.1016/j.jretai.2008.11.001
Wang, J., Wang, J., Athiwaratkun, B., Zhang, C., & Zou, J. (2024). Mixture-of-Agents enhances large language model capabilities. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.04692
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., Li, B., Jiang, L., Zhang, X., & Wang, C. (2023). AutoGen: Enabling Next-Gen LLM applications via Multi-Agent Conversation. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2308.08155
Xu, H., Liu, B., Shu, L., & Yu, P. S. (2019). BERT Post-Training for review reading comprehension and aspect-based sentiment analysis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1904.02232
Zablah, A. R., Bellenger, D. N., & Johnston, W. J. (2004). An evaluation of divergent perspectives on customer relationship management: Towards a common understanding of an emerging phenomenon. Industrial Marketing Management, 33(6), 475–489. https://doi.org/10.1016/j.indmarman.2004.01.006