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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien Chin Chen) | |
| dc.contributor.author | You-De Tseng | en |
| dc.contributor.author | 曾有德 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:25:19Z | - |
| dc.date.available | 2009-08-11 | |
| dc.date.copyright | 2009-08-11 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-22 | |
| dc.identifier.citation | [1] Chevalier, J. A. and Mayzlin, D. “The Effect of Word of Mouth on Sales: Online Book Reviews,” Journal of Marketing Research, 43(3), pp. 345–354, 2006.
[2] Dave, K., Lawrence, S., and Pennock, D. M. “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews,” in Proceedings of the 12th International Conference on World Wide Web, pp. 519–528, 2003. [3] Ding, X., Liu, B., and Yu, P. S. “A Holistic Lexicon-Based Approach to Opinion Mining,” in Proceedings of the international conference on Web Search and web Data Mining, pp. 231–240, 2008. [4] Eppler, M. J. and Wittig, D. “Conceptualizing Information Quality: A Review of Information Quality Frameworks from the Last Ten Years,” in Proceedings of the 2000 Conference on Information Quality, pp. 83–96, 2000. [5] Fellbaum, C. “WordNet: an Electronic Lexical Database. Cambridge,” MA: MIT Press, 1998. [6] He, B., Macdonald, C., He, J., and Ounis, I. “An Effective Statistical Approach to Blog Post Opinion Retrieval,” in Proceedings of the 17th ACM conference on Information and knowledge management, pp. 1063–1072, 2008. [7] Hsu, C. W. and Lin, C. J. “A Comparison of Methods for Multiclass Support Vector Machines,” IEEE Transactions on Neural Networks, 13(2), pp. 415–425, 2002. [8] Hu, M. and Liu, B. “Mining and Summarizing Customer Reviews,” in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168–177, 2004. [9] Huang, K. T., Lee, Y. W., and Wang, R. Y. Quality Information and Knowledge, NJ, USA: Prentice Hall PTR Upper Saddle River, 1998. [10] Jindal, N. and Liu, B. “Opinion Spam and Analysis,” in Proceedings of the international conference on Web Search and web Data Mining, pp. 219–230, 2008. [11] Joachims, T. “Making Large-scale SVM Learning Practical,” In B. Schokopf and C. Burges and A. Smola (ed.), Advances in kernel methods: support vector learning, pp. 169-184, 1999. [12] Jones, K. S., Walker, S., and Robertson, S. E. “A probabilistic model of information retrieval: development and comparative experiments: Part 1,” Information Processing and Management, 36(6), pp. 779–808, 2000. [13] Kim, S. M. and Hovy, E. “Determining the Sentiment of Opinions,” in Proceedings of the 20th international conference on Computational Linguistics, pp. 1367–1373, 2004. [14] Kim, S. M., Pantel, P., Chklovski, T., and Pennacchiotti, M. “Automatically Assessing Review Helpfulness,” in Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 423–430, 2006. [15] Ku, L. W., Liang, Y. T., and Chen, H. H. “Opinion Extraction, Summarization and Tracking in News and Blog Corpora,” in Proceedings of the AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, pp. 100–107, 2006. [16] Ling, C., and Li, C. “Data Mining for Direct Marketing: Problems and Solution,” in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD’98), pp. 73–79, 1998. [17] Liu, B., Hu, M., and Cheng, J. “Opinion Observer: Analyzing and Comparing Opinions on the Web,” in Proceedings of the 14th international conference on World Wide Web, pp. 342–351, 2005. [18] Liu, J., Cao, Y., Lin, C. Y., Huang, Y., and Zhou, M. “Low-Quality Product Review Detection in Opinion Summarization,” in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 334–342, 2007. [19] Liu, S., Liu, F., Yu, C., and Meng, W. “An Effective Approach to Document Retrieval via Utilizing WordNet and Recognizing Phrases,” in Proceeding of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 266–272, 2004. [20] Manning, C., Raghavan, P., and Schutze, H. An Introduction to Information Retrieval. Cambridge, England: Cambridge University Press, 2008. [21] Miller, G., Beckwith, R., Fellbaum, C., Gross, D., and Miller, K. “Introduction to WordNet: An On-line Lexical Database,” International Journal of Lexicography, 3(4), pp. 235–244, 1990. [22] Pang, B., Lee, L., and Vaithyanathan, S. “Thumbs up? Sentiment Classification using Machine Learning Techniques,” in Proceedings of the ACL-02 conference on Empirical Methods in Natural Language Processing, pp. 79–86, 2002. [23] Roed, J. “Language Learner Behavior in a Virtual Environment,” Computer Assisted Language Learning, 16(2–3), pp. 155–172, 2003. [24] Steinwart, I. and Christmann, A. Support Vector Machine,. New York: Springer, 2008. [25] Tsochantaridis, I., Joachims, T., Hofmann, T., and Altun, Y. “Large Margin Methods for Structured and Interdependent Output Variables,” Journal of Machine Learning Research, 6(Sep), pp. 453-1484, 2006. [26] Turney, P.D. “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 129–159, 2002. [27] Wang, R. Y. and Strong, D. M. “Beyond Accuracy: What Data Quality Means to Data Consumers,” Journal of Management Information Systems, 12(4), pp. 5–33, 1996. [28] Weston. J. and Watkins. C. “Multi-class Support Vector Machines,” Technical Report CSD-TR-98-04, Royal Holloway, University of London, Department of Computer Science, 1998. [29] Zhang, Z. and Varadarajan, B. “Utility Scoring of Product Reviews,” in Proceedings of the 15th ACM international Conference on Information and Knowledge Management, pp. 51–57, 2006. [30] Zhang, W., Yu, C., and Meng, W. “Opinion Retrieval from Blogs,” in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp. 831–840, 2007. [31] Zhang, M. and Ye, X. “A Generation Model to Unify Topic Relevance and Lexicon-based Sentiment for Opinion Retrieval,” in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 411–418, 2008. [32] Zhu, X. and Gauch, S. “Incorporating Quality Metrics in Centralized/Distributed Information Retrieval on the World Wide Web,” in Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in Information Retrieval, pp. 288–295, 2000. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42835 | - |
| dc.description.abstract | Web2.0的盛行使得網際網路成為重要的商業資訊來源，透過許多電子商務網站所提供的商品評論平台，網際網路使用者可自由地撰寫商品相關的評論，正面的商品評論可幫助消費者制定商品的購買決策，而負面的商品評論可協助企業檢討與修正商品的商業策略。但隨著評論數量快速地增長，消費者與企業均需要有效的資料探勘技術來由大量的文字資訊中找出重要的評論意見。現行的評論意見探勘技術多忽略了評論內容的資訊品質，以致於探勘出的評論其資訊品質良莠不齊。在本研究中，我們提出一套方法來評估商品評論的資訊品質，我們將資訊品質評估視為一種分類問題，並使用一套有效的資訊品質架構來萃取重要的評論資訊特徵。實驗結果顯示我們提出的方法有優異的資訊品質評估效能，而且顯著地優於其它學者在近幾年所提出的方法。此外本研究還進行升力曲線分析找出高品質評論所具備的重要因素。最後我們提出一個以評論品質分類器為基礎的評論檢索雛型系統，來幫助使用者有效地搜尋到包含他們需要的有用資訊之評論。 | zh_TW |
| dc.description.abstract | The ubiquity of Web 2.0 makes the Internet an invaluable source of business information. For instance, product reviews composed collaboratively by many independent Internet reviewers can help consumers make purchase decisions and enable enterprises to improve their business strategies. As the number of reviews is increasing exponentially, opinion mining is needed to identify important reviews and opinions to answer users’ queries. Most opinion mining approaches try to extract sentimental or bipolar expressions from a large volume of reviews. However, the mining process often ignores the quality of each review and may retrieve useless or even noisy documents. In this thesis, we propose a method for evaluating the quality of information in product reviews. We treat the evaluation of review quality as a classification problem and employ an effective information quality framework to extract representative review features. Experiments based on an expert-composed data corpus demonstrate that the proposed method outperforms state-of-the-art approaches significantly. Moreover, this thesis implements detailed lift analyses to find the important factors for constructing high-quality reviews. Finally, we propose a prototype of review retrieval system that based on the classifier of review quality to help users to efficiently search the reviews that contain helpful information they want. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:25:19Z (GMT). No. of bitstreams: 1 ntu-98-R96725044-1.pdf: 998059 bytes, checksum: 4447a484d88ef2d9c9882eed2495f1ca (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | 論文摘要 i
THESIS ABSTRACT ii Table of Contents iii List of Figures v List of Tables vi Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Objectives, Approaches, and Results 4 1.4 Thesis Organization 5 Chapter 2 Related Works 6 2.1 Opinion mining 6 2.1.1 Opinion Extraction and Polarity Identification 6 2.1.2 Opinion Target Identification 7 2.2 Opinion Retrieval 8 2.3 Review Quality Evaluation 9 2.3.1 Ranking-based methods 10 2.3.2 Classification-based method 11 Chapter 3 Methods 13 3.1 Definition of Review Quality 13 3.2 Information Quality-based Review Features 15 3.3 Classification Models 19 3.3.1 The Binary SVM 20 3.3.2 The Kernels 23 3.3.3 Multiclass SVMs 25 Chapter 4 Performance Evaluations 27 4.1 Data Preprocessing and Annotation 27 4.1.1 Description of Data Annotation 27 4.1.2 Evaluating the Agreement of Annotations 28 4.1.3 Experiments Design 31 4.1.4 Metrics for Evaluating Performance 31 4.2 IQ Dimension Evaluations 34 4.3 Comparisons with Other Methods 37 4.4 High-Quality Review Analysis 39 Chapter 5 Quality-based Review Retrieval System 44 5.1 Data Preprocessing 45 5.2 Classification Model Construction 46 5.3 Review Quality Evaluation 46 5.4 Review Ranking and Retrieval 46 Chapter 6 Conclusions 49 6.1 Contributions 49 6.2 Future Works 50 References 52 Appendix 1. The Definitions of Wang and Strong’s IQ Framework [27] 55 | |
| dc.language.iso | en | |
| dc.subject | 評論檢索系統 | zh_TW |
| dc.subject | 文件探勘 | zh_TW |
| dc.subject | 分類 | zh_TW |
| dc.subject | 意見探勘 | zh_TW |
| dc.subject | 資訊品質 | zh_TW |
| dc.subject | 商品評論 | zh_TW |
| dc.subject | 支撐向量機 | zh_TW |
| dc.subject | information quality | en |
| dc.subject | review retrieval system | en |
| dc.subject | support vector machine | en |
| dc.subject | product reviews | en |
| dc.subject | text mining | en |
| dc.subject | classification | en |
| dc.subject | opinion mining | en |
| dc.title | 應用資訊品質架構於商品評論品質評估 | zh_TW |
| dc.title | Quality Evaluation of Product Reviews Using an Information Quality Framework | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡益坤(Yih-Kuen Tsay),陳孟彰(Meng Chang Chen) | |
| dc.subject.keyword | 文件探勘,分類,意見探勘,資訊品質,商品評論,支撐向量機,評論檢索系統, | zh_TW |
| dc.subject.keyword | text mining,classification,opinion mining,information quality,product reviews,support vector machine,review retrieval system, | en |
| dc.relation.page | 55 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-07-23 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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