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論文名稱 Title |
從使用者評論探勘競爭者 Competitor Mining Using Customer Reviews |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
48 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2016-07-08 |
繳交日期 Date of Submission |
2016-07-27 |
關鍵字 Keywords |
文字探勘、機器學習、競爭者辨識、顧客評論、比較意見 competitor identification, text mining, machine learning, comparative opinions, customer reviews |
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統計 Statistics |
本論文已被瀏覽 5966 次,被下載 121 次 The thesis/dissertation has been browsed 5966 times, has been downloaded 121 times. |
中文摘要 |
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。 |
Abstract |
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. |
目次 Table of Contents |
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 References |
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 |
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