消費者的需求是企業產品創新、服務改善的重要依據。過去企業透過消費者與銷售人員的互動、專家訪談、問卷調查等方式了解消費者。隨著網路技術的蓬勃發展，許多消費者在網路上發表評論使得企業有不同的管道了解消費者，但大量的網路資訊難以被快速歸納，導致企業無法作出快速正確的決策。是故，本研究利用詞性組合以及機器學習中分群、分類等概念設計一自動化之消費者觀點獲取模式，藉此模式自動歸納網路評論得到一消費者觀點架構，透過此架構企業可以快速了解網路內容中潛藏的消費者需求，為企業帶來競爭優勢。

Consumer demand is an important basis for product innovation and service improvement. Traditionally, consumer demands are identified through the interactions of sale staff with consumers, expert interviews and questionnaires. With the rapid development of network technology and various social networking, enterprises may be able to understand consumer demands through internet review. However, there still exists technological issues to obtain valuable information from massive network data. Therefore, a systematic methodology to identify enterprise key customer opinions on internet is necessary.
This research first designed an automatic consumer perspective integration model via the concept of speech combination, machine learning: clustering and classification. A consumer perspective acquisition mechanism was then developed based on this model. With this mechanism, enterprises are able to obtain closer consumer opinions and thus capable of supplying more customer-desired products, and standing on an advantageous, competitive position in advance.

參考文獻
1. Buttle, Francis A. (1998). Word of mouth: understanding and managing referral marketing. Journal of strategic marketing, 6(3), 241-254.
2. Bo Pang, Lillian Lee. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting on Association for Computational Linguistics,271-278. Association for Computational Linguistics.
3. Christy M.K. Cheung, Matthew K.O. Lee, Neil Rabjohn. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Research, 18(3), 229-247.
4. Changli Zhang, Daniel Zeng, Jiexun Li, Fei-Yue Wang, Wanli Zuo. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60(12), 2474-2487.
5. Doris H Kincade, Dianna Vass, Cassill, Nancy L Cassill. (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.
6. Elihu Katz, Paul F. Lazarsfeld. (1955). Personal Influence, Glencoe, Ill.: Free Press, 162-208;
7. E.W.T. Ngai, Li Xiu, D.C.K. Chau. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592-2602.
8. Evangelos Simoudis. (1996). Reality check for data mining. IEEE Expert: Intelligent systems and their applications, 11(5), 26-33.
9. Fan, C. K. and Tsai, W. H. (1988). Automatic Word Identification in Chinese Sentences by the Relaxation Technique. Computer Proceeding of Chinese and Oriental Languages, 33-56.
10. Garcia-Murillo M, Annabi, H. (2002). Customer knowledge management. Journal of the Operational Research Society, 53(8), 875-884.
11. Gray A Knight, S Tamar Cavusgil. (2004). Innovation, organizational capabilities, and the born-global firm. Journal of International Business Studies, 35(2), 124-141.
12. Helen S. Du, Christian Wagner. (2006). Weblog success: Exploring the role of technology. International Journal of Human-Computer Studies, 64(9), 789-798.
13. Henry Chesbrough, Jim Spohrer. (2006). A research manifesto for services science. Communications of the ACM, 49(7), 35-40.
14. Honglei Guo, Huijia Zhu, Guo, Zhili Guo, XiaoXun Zhang, Zhong Su. (2009). Product feature categorization with multilevel latent semantic association. InProceedings of the 18th ACM conference on Information and knowledge management , 1087-1096. ACM.
15. Jingnian Chen, Houkuan Huang, Shengfeng Tian, Youli Qu. (2009). Feature selection for text classification with Naïve Bayes. Expert Systems with Applications, 36(3), 5432-5435.
16. Johan Arndt. (1967). Role of product-related conversations in the diffusion of a new product. Journal of marketing Research, 4(3), 291-295.
17. Jo Brown, Amanda J. Broderick, Nick Lee. (2007). Word of mouth communication within online communities: Conceptualizing the online social network. Journal of interactive marketing, 21(3), 2-20.
18. James A. Fitzsimmons, Mona J. Fitzsimmons. (1994). Service Management for Competitive Advantage, McGraw-Hill Ryerson.
19. James L. Heskett, W. Earl Sasser Jnr, Leonard A. Schlesinger. (1997). The service profit chain: How leading companies link profit and growth to loyalty. Satisfaction, and Value, New York.
20. Jun Zhao, Feifan Liu. (2008). Product named entity recognition in Chinese text. Language Resources and Evaluation, 42(2), 197-217.
21. Jian-Yun Nie, Martin Brisebois, Xiaobo Ren. (1996). On Chinese Text Retrieval. Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, 225-233. ACM
22. Likert R. (1932). A technique for the measurement of attitudes. Archives of psychology.140(22).
23. Michael Chau, Jennifer Xu. (2007). Mining communities and their relationships in blogs: A study of online hate groups. International Journal of Human-Computer Studies, 65(1), 57-70.
24. Masoud Makrehchi, Mohamed S. Kamel. (2007). Automatic taxonomy extraction using google and term dependency. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp.321-325. IEEE Computer Society.
25. Nikolay Archak, Anindya Ghose, Panagiotis G. Ipeirotis. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485-1509.
26. Oded Netzer, Ronen Feldman, Jacob Goldenberg, Moshe Fresko. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
27. Ping-I Chen, Shi-Jen Lin. (2010). Automatic keyword prediction using Google similarity distance. Expert Systems with Applications, 37(3), 1928-1938.
28. Pamela Kiecker, Deborah Cowles. (2002). Interpersonal communication and personal influence on the Internet: A framework for examining online word-of-mouth. Journal of Euromarketing, 11(2), 71-88.
29. Paul P. Maglio, Savitha Srinivasan, Jeffrey T. Kreulen, Jim Spohrer. (2006). Service systems, service scientists, SSME, and innovation. Communications of the ACM, 49(7), 81-85.
30. Salton, G., Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513-523.
31. Su, J.-H., Huang, W.-J., Yu, P. S., & Tseng, V. S. (2011). Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns. Knowledge and Data Engineering, IEEE Transactions on, 23(3), 360-372.
32. Rudi L. Cilibrasi, Vitanyi, Paul M.B. Vitanyi. (2007). The google similarity distance. Knowledge and Data Engineering, IEEE Transactions on, 19(3), 370-383.
33. Ronen Feldman, Ido Dagan. (1995). Knowledge Discovery in Textual Databases (KDT). In Proc.of the First Int. Conf. on Knowledge Discovery (KDD), 112-117.
34. Thorsten Hennig-Thurau, Kevin P Gwinner, Gianfranco Walsh, Dwayne D. Gremler. (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.
35. Rui Xia, Chengqing Zong, Shoushan Li. (2011). Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences, 181(6), 1138-1152..
36. Richard Sproat, Chilin Shih. (1990). A Statistical Method for Finding Word Boundaries in Chinese Text. Computer Processing of Chinese and Oriental Languages,4(4), 336-351.
37. Richard Tzong-Han Tsai, Chun-Hui Chou. (2011). Extracting dish names from chinese blog reviews using suffix arrays and a multi-modal CRF model. In First International Workshop on Entity-Oriented Search, ACM SIGIR, 7-13.
38. Shin-ichi Todoroki, Tomoya Konishi, Satoru Inoue. (2006). Blog-based research notebook: personal informatics workbench for high-throughput experimentation. Applied Surface Science, 252(7), 2640-2645.
39. Thomas W. Gruen, Talai Osmonbekov, Andrew J. Czaplewski. (2006). eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty. Journal of Business research, 59(4), 449-456.
40. Thorsten Hennig-Thurau, Gianfranco Walsh. (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.
41. Werner Reinartz, Manfred Krafft, Wayne D. Hoyer. (2004). The customer relationship management process: its measurement and impact on performance. Journal of marketing research, 293-305.
42. Wei Lee Woon, Stuart Madnick. (2009). Asymmetric information distances for automated taxonomy construction. Knowledge and information systems, 21(1), 91-111.
43. Yu-Shan Lin, Jun-Ying Huang. (2006). Internet blogs as a tourism marketing medium: A case study. Journal of Business Research, 59(10), 1201-1205.
44. Yichen Lin, Hwan-Yann Su, Shihen Chien. (2006). A knowledge-enabled procedure for customer relationship management. Industrial Marketing Management, 35(4), 446-456.
45. Yao Pan, Yu Wang. (2011). Mining product features and opinions based on pattern matching. In Computer Science and Network Technology (ICCSNT), 2011 International Conference on , 3, 1901-1905. IEEE.
46. Zhongwu Zhai , Bing Liu, Hua Xu, Peifa Jia. (2011). Clustering product features for opinion mining. In Proceedings of the fourth ACM international conference on Web search and data mining, 347-354. ACM.