博碩士論文 etd-0627116-120111 詳細資訊


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姓名 林漪寒(Yi-Han Lin) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 碩士(Master) 畢業時期 104學年第2學期
論文名稱(中) 從使用者評論探勘競爭者
論文名稱(英) Competitor Mining Using Customer Reviews
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    紙本論文:3 年後公開 (2019-07-27 公開)

    電子論文:使用者自訂權限:校內 3 年後、校外 3 年後公開

    論文語文/頁數 英文/48
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    摘要(中) 對於企業而言,競爭者辨識扮演相當重要的角色,並影響著企業成功與否。面對眾多的競爭者,公司必須能夠正確地辨識出他們,才能夠制定相對應的競爭策略,並且抵抗競爭者的入侵。在我們的研究中,我們透過文字探勘以及機器學習的方式,協助企業辨識競爭者。在顧客評論中,顧客時常會比較兩項產品或公司。因此,我們可以利用這些評論裡的比較意見,計算兩間公司被顧客共同比較的次數,以及評論者偏好的公司。接著,我們透過網路的方式去呈現公司跟公司之間的競爭關係。最後,我們根據上述計算的共同比較次數以及偏好,去建立分類器,分類兩間公司之間是否為競爭者。最後,我們將會評估我們提出的方法。
    摘要(英) Competitor identification plays an important role in the success of a company. Companies have to identify their competitors to make the business strategies before confronting them. In this work, we propose a text mining and machine learning method to identify competitors for companies. Using the comparative opinions in customer reviews, we attempt to obtain the various information, including amount of co-occurrence and sentiment, associated with a pair of companies. Then, we build a classifier to identify competitors based on the co-occurrence and sentiment information. We finally demonstrate and evaluate our proposed method.
    關鍵字(中)
  • 文字探勘
  • 機器學習
  • 競爭者辨識
  • 顧客評論
  • 比較意見
  • 關鍵字(英)
  • competitor identification
  • text mining
  • machine learning
  • comparative opinions
  • customer reviews
  • 論文目次 CHAPTER 1 - Introduction 1
    1.1 Background and Motivation 1
    1.2 Research Purpose 2
    1.3 Contribution 3
    1.4 Thesis Organization 3
    CHAPTER 2 – Literature Review 4
    2.1 Managerial Aspect 4
    2.2 Technical Aspect 6
    2.2.1 Comparative Sentence Mining 6
    2.2.2 Competitor Mining 7
    CHAPTER 3 – Problem Definition 9
    CHAPTER 4 – Methodology 11
    4.1 Overall Process 11
    4.2 Data Collection 12
    4.3 Relationships Extraction 14
    4.4 Construct the Candidate Competitor Pairs 22
    4.5 Build a Classification Model 24
    CHAPTER 5 – Performance Evaluation 26
    5.1 The Gold-standard Data of Competitors 26
    5.2 The Candidate Competitor Pairs 29
    5.3 Four Evaluation Metrics 30
    5.4 Compare Various Classification Methods 31
    5.5 Performance Results 32
    CHAPTER 6 – Conclusion 38
    References 39
    參考文獻 Bao, S., Li, R., Yu, Y., & Cao, Y. (2008). Competitor mining with the web. IEEE Transactions on Knowledge and Data Engineering, 20(10), 1297–1310. http://doi.org/10.1109/TKDE.2008.98
    Bergen, M., & Peteraf, M. a. (2002). Competitor Identification and Competitor Analysis: A Broad-Based Managerial Approach. Managerial and Decision Economics, 23(4-5), 157–169. http://doi.org/10.1002/mde.1059
    Chen, M.-J. (1996). Competitor Analysis and Interfirm Integration Rivalry: Toward a Theoretical Integration. The Academy of Management Review, 21(1), 100–134. http://doi.org/10.5465/AMR.1996.9602161567
    Chen, M.-J., & MacMillan, I. C. (1992). Nonresponse and Delayed Response To Competitive Moves: the Roles of Competitor Dependence and Action Irreversibility. Academy of Management Journal, 35(3), 539–570. http://doi.org/10.2307/256486
    Ganapathibhotla, M., & Liu, B. (2008). Mining opinions in comparative sentences. In Proceedings of the 22nd International Conference on Computational Linguistics - COLING ’08 (Vol. 1, pp. 241–248). http://doi.org/10.3115/1599081.1599112
    Jindal, N., & Liu, B. (2006a). Identifying comparative sentences in text documents. Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’06, 244. http://doi.org/10.1145/1148170.1148215
    Jindal, N., & Liu, B. (2006b). Mining Comparative Sentences and Relations. AAAI, 22, 1331–1336. http://doi.org/10.1107/S0108270189000326
    Lappas, T., Valkanas, G., & Gunopulos, D. (2012). Efficient and domain-invariant competitor mining. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 408–416). http://doi.org/10.1145/2339530.2339599
    Ma, Z., Pant, G., & Sheng, O. R. L. (2011). Mining competitor relationships from online news: A network-based approach. Electronic Commerce Research and Applications, 10(4), 418–427. http://doi.org/10.1016/j.elerap.2010.11.006
    McAuley, J., Pandey, R., & Leskovec, J. (2015). Inferring Networks of Substitutable and Complementary Products. In Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’15) (p. 12). http://doi.org/10.1145/2783258.2783381
    McAuley, J., Targett, C., Shi, Q., & Hengel, A. Van Den. (2015). Image-based Recommendations on Styles and Substitutes. Proceeding of 38th ACM SIGIR, 1–11. http://doi.org/10.1145/2766462.2767755
    Pant, G., & Sheng, O. R. L. (2009). Avoiding the Blind Spots: Competitor Identification Using Web Text and Linkage Structure. In Thirtieth International Conference on Information Systems. Retrieved from http://aisel.aisnet.org/icis2009 Recommended
    Peteraf, M. A., & Bergen, M. E. (2003). Scanning dynamic competitive landscapes: A market-based and resource-based framework. Strategic Management Journal, 24(10 SPEC ISS.), 1027–1041. http://doi.org/10.1002/smj.325
    Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and companies. New York. http://doi.org/10.1002/smj.4250020110
    Xu, K., Liao, S. S., Li, J., & Song, Y. (2011). Mining comparative opinions from customer reviews for Competitive Intelligence. Decision Support Systems, 50(4), 743–754. http://doi.org/10.1016/j.dss.2010.08.021
    口試委員
  • 魏志平 - 召集委員
  • 康藝晃 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2016-07-08 繳交日期 2016-07-27

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