良好的企業商品創新與改良，為企業能永續經營成長的重要因素之一，過去企業透過專家訪談、問卷調查及與消費者互動等方式了解消費者需求藉以進行商品創新。但隨著網路技術的蓬勃發展，許多消費者在網路上發表商品有關的評論及進行討論，使得企業有更多元的的管道了解消費者的需求，但大量的網路資訊難以被快速歸納，導致企業無法針對快速變動的市場變化作出快速且正確的決策。
本研究之目的為發展一以網路口碑為基之商品機會探索的方法，再依此開發相關實現技術，以提供企業了解目前網路上之熱門話題以及從這些討論中來獲取商品創新的機會。本方法從大型網路討論區的討論文章精煉產生出事件模型，並從中分析出熱門事件模型，再從與商品有關的討論文章分析並產生商品模型。
本研究以KeyGraph概念混和語意網路(semantic network)為基礎，發展一「商品創新機會探索」方法，以提供企業了解目前網路上之熱門話題以及從這些討論中來獲取商品創新的機會。本方法將收集到網路口碑進行結構化建立出事件Semantic-KeyGraph，再以密度為基的聚類分析(DBSCAN)分析出熱門的事件，透過擴散性思考及詞彙聯想的原則，進行事件與商品間的商品創新機會探索。此方法可提供企業進行商品創新開發可能的機會，亦可提供企業快速的掌握市場脈動以及提快速了解自身商品在於目前市場中所處的狀態。

Good enterprise product innovation and improvement, are an important factor in business growth. In the past, enterprise through expert interviews, questionnaires and consumers interact to understand consumer demand thereby commodity innovation. But with the rapid development of Internet technology, many consumers in the online publication commodity-related comments and discussion, so that enterprises have more diversified pipeline of understanding of consumer needs, but a large number of network information is difficult to be quickly summarized , result in the inability to make quick and correct decisions for the market changes rapidly changing.
This research attempts to apply KeyGraph concept blending semantic network, development of an " eWOM-based Chance Discovery for Product Innovation" mechanism, providing enterprise understand those hot discussions on the web and find chance of product innovation from these discussions.
Through KeyGraph plus concept of semantic network, the collected ewom establish a structured event Semantic-KeyGraph, cluster analysis (DBSCAN) density-based analysis of the popular event.Chance discovery for product innovation between popular event and product through the principle of divergent thinking and word association. This mechanism provides enterprise product innovation potential chance, can also provide enterprises to quickly grasp the pulse of the market and know their product’s state of the market is in.

一、英文文獻:
Abrahams, A. S., Jiao, J., Wang, G. A., & Fan, W. (2012). Vehicle defect discovery from social media. Decision Support Systems, 54(1), 87-97.
Arndt, J. (1967). Role of product-related conversations in the diffusion of a new product. Journal of marketing Research, 291-295.
Brown, J., Broderick, A. J., & Lee, N. (2007). Word of mouth communication within online communities: Conceptualizing the online social network. Journal of interactive marketing, 21(3), 2-20.
Chen, H., Chau, M., & Zeng, D. (2002). CI Spider: a tool for competitive intelligence on the Web. Decision Support Systems, 34(1), 1-17.
Chen, P. I., & Lin, S. J. (2010). Automatic keyword prediction using Google similarity distance. Expert Systems with Applications, 37(3), 1928-1938.
Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Research, 18(3), 229-247.
Cilibrasi, R. L., & Vitanyi, P. M. (2007). The google similarity distance. IEEE Transactions on knowledge and data engineering, 19(3), 370-383.
Cilibrasi, R. L., & Vitanyi, P. M. (2007). The google similarity distance. IEEE Transactions on knowledge and data engineering, 19(3), 370-383.
Clyde A. Warden, (2006),Effects of Negative Environmental Events on Shopping Behavior: A Longitudinal Study.
Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007, May). Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web (pp. 271-280). ACM.
Drucker, P. F. (2006). Classic Drucker: essential wisdom of Peter Drucker from the pages of Harvard Business Review. Harvard Business Press.
Fisher, M., Hammond, J., Obermeyer, W., & Raman, A. (1997). Configuring a supply chain to reduce the cost of demand uncertainty. Production and operations management, 6(3), 211-225.
Frantzi, K., Ananiadou, S., & Mima, H. (2000). Automatic recognition of multi-word terms:. the c-value/nc-value method. International Journal on Digital Libraries, 3(2), 115-130.
Fu, M. H., Peng, C. H., Kuo, Y. H., & Lee, K. R. (2012, June). Hidden community detection based on microblog by opinion-consistent analysis. In Information Society (i-Society), 2012 International Conference on (pp. 83-88). IEEE.
Garcia-Murillo, M., & Annabi, H. (2002). Customer knowledge management. Journal of the Operational Research society, 53(8), 875-884.
Guilford, J. P. (1967). The nature of human intelligence.
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.
Hennig-Thurau, T., Walsh, G., & Walsh, G. (2003). Electronic word-of-mouth: Motives for and consequences of reading customer articulations on the Internet. International Journal of Electronic Commerce, 8(2), 51-74.
Heskett, J. L., Sasser, W. E., & Schlesinger, L. A. (1997). The service profit chain. New York.
Holley, C. D., & Dansereau, D. F. (Eds.). (2014). Spatial learning strategies: Techniques, applications, and related issues. Academic Press.
Hong, C. F. (2009). Qualitative chance discovery–Extracting competitive advantages. Information Sciences, 179(11), 1570-1583.
Katz, E., & Lazarsfeld, P. F. (1966). Personal Influence, The part played by people in the flow of mass communications. Transaction Publishers.
Kincade, D. H., Vass, D., & Cassill, N. L. (2001). Implementation of technology and relationships to supply chain performance: apparel manufacturers' perspectives. The International Review of Retail, Distribution and Consumer Research, 11(3), 301-327.
Liu, H., He, J., Wang, T., Song, W., & Du, X. (2013). Combining user preferences and user opinions for accurate recommendation. Electronic Commerce Research and Applications, 12(1), 14-23.
Lo, S. (2008). Web service quality control based on text mining using support vector machine. Expert Systems with Applications, 34(1), 603-610.
Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of Finance, 66(1), 35-65.
Makrehchi, M., & Kamel, M. S. (2007, November). Automatic taxonomy extraction using google and term dependency. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (pp. 321-325). IEEE Computer Society.
Malone, T. W., Yates, J., & Benjamin, R. I. (1987). Electronic markets and electronic hierarchies. Communications of the ACM, 30(6), 484-497.
Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business horizons, 52(4), 357-365.
Nguyen, H. T., & Cao, T. H. (2008, July). Named entity disambiguation on an ontology enriched by Wikipedia. In Research, Innovation and Vision for the Future, 2008. RIVF 2008. IEEE International Conference on (pp. 247-254). IEEE.
Nic, J. Y. and M. Briscobois.( 1996).On Chinese Text Retrieval. Proceedingof SIGIR, pp. 225-233.
Ohsawa, Y. (2002). Chance discoveries for making decisions in complex real world. New Generation Computing, 20(2), 143-163.
Ohsawa, Y. (2002). Introduction to chance discovery. Journal of Contingencies and Crisis Management, 10(2), 61-62.
Ohsawa, Y., & Nara, Y. (2002). Modeling the process of chance discovery by chance discovery on double helix. In Proc. of AAAI Fall Symposium on Chance Discovery (pp. 33-40).
Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 12.
Sheu, D. D. (2010). A Classification and Process of Systematic Innovation. International Journal of Systematic Innovation, 1(1).
Woon, W. L., & Madnick, S. (2009). Asymmetric information distances for automated taxonomy construction. Knowledge and information systems, 21(1), 91-111.
Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., ... & Zhou, Z. H. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1), 1-37.
Zhang, Y., Dang, Y., Chen, H., Thurmond, M., & Larson, C. (2009). Automatic online news monitoring and classification for syndromic surveillance. Decision Support Systems, 47(4), 508-517.
二、中文文獻:
许鑫, 俞飞, & 张莉. (2011). 一种文本倾向性分析方法及其应用. 现代图书情报技术, 27(10), 54-62.
余民寧. (1997). 有意義的學習-概念構圖之研究. 台北: 商鼎文化, 9.
游和正. (2012). 領域相關詞彙極性分析及文件情緒分類之研究. 臺灣大學資訊工程學研究所學位論文, 1-57.
黃純敏, 李亞哲, & 陳柏宏. (2015). 以維基百科為基礎之中文縮寫詞與同義詞庫建構. 資訊管理學報, 22(2), 117-140.
三、網路參考:
CKIP 中文斷詞系統， http://ckipsvr.iis.sinica.edu.tw/
「東東同義詞詞典」http://www.kwuntung.net/synonym/