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  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 資訊管理學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61529
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DC 欄位值語言
dc.contributor.advisor李瑞庭
dc.contributor.authorChao-Hung Chenen
dc.contributor.author陳昭宏zh_TW
dc.date.accessioned2021-06-16T13:05:04Z-
dc.date.available2018-08-28
dc.date.copyright2013-08-28
dc.date.issued2013
dc.date.submitted2013-08-05
dc.identifier.citation[1] C. Albornoz, J. Plaza and L. Gerv’as, A hybrid approach to emotional sentence polarity and intensity classification, Proceedings of 14th Conference on Natural Language Learning, pp. 153-161, 2010.
[2] A. Bhatnagart and S. Ghose, A latent class segmentation analysis of e-shoppers, Journal of Business Research, Volume 57, Issue 7, pp. 758-767, 2004.
[3] S. Bose and N. Gupta, Customer perception of services based on the SERVQUAL dimensions: A study of Indian commercial banks, Journal of Services Marketing Quarterly, Volume 34, Issue 1, pp. 49-66, 2013.
[4] D. Buscaldi, J. Le Roux, J. J. G. Flores and A. Popescu, LIPN-CORE: Semantic text similarity using n-grams, WordNet, syntactic analysis, ESA and information retrieval based features, Proceedings of Second Joint Conference on Lexical and Computational Semantics, pp. 162-168, 2013.
[5] J. C. Albornoz, L. Plaza, P. Gerv’as and A. D’iaz, A joint model of feature mining and sentiment analysis for product review rating, Proceedings of European Conference on Information Retrieval, pp. 55-66, 2011.
[6] Y. Chang, C. Chang, K. Chen, C. Lei, Radar chart: Scanning for satisfactory QoE in QoS dimensions, Journal of IEEE Network, Volume 26, Issue 4, pp. 25-31, 2012.
[7] B. Furlan, V. Batanović and B. Nikolić, Semantic similarity of short texts in languages with a deficient natural language processing support, Decision Support Systems, Volume 55, Issue 3, pp. 710-719, 2013.
[8] P. Green, Marketing applications of MDS: Assessment and outlook, Journal of Marketing, Volume 39 , Issue 1, pp. 24-31, 1975
[9] H. Guo, H. Zhu, Z. Guo, X.-X. Zhang and Z. Su, Product feature categorization with multilevel latent semantic association, Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1087-1096, 2009.
[10] W.M. Han, S.J. Huang, An empirical analysis of risk components and performance on software projects, Journal of Systems and Software, Volume 80, Issue 1, pp.42-50, 2007
[11] M. Hu and B. Liu, Mining and summarizing customer reviews, Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168-177, 2004.
[12] T. Lappas, G. Valkanas and D. Gunopulos, Efficient and domain-invariant competitor mining, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 408-416, 2011.
[13] B. Liu, M. Hu and J. Cheng, Opinion observer: Analyzing and comparing opinions on the web, Proceedings of the 14th International Conference on World Wide Web, pp. 342-351, 2005.
[14] D. M. Blei, A. Y. Ng and M. I. Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research, Volume 3, pp. 993-1022, 2003.
[15] Y. Matsuo, T. Sakaki, K. Uchiyama and M. Ishizuka, Graph-based word clustering using a web search engine, Proceedings of the Conference on Empirical Methods in Natural Language, pp. 542-550, 2006.
[16] O. Netzer, R. Feldman, J. Goldenberg and M. Fresko, Mine your own business: Market structure surveillance through text mining, Journal of Marketing Science, Volume 31, Issue 3, pp. 521-543, 2012.
[17] T. Pedersen, Information content measures of semantic similarity, Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.329-332, 2010.
[18] A. Popescu and O. Etzioni, Extracting product features and opinions from reviews, Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 339-346, 2005.
[19] R. Schmalensee, Perceptual maps and the optimal location of new products: An integrative essay, International Journal of Research in Marketing, Volume 5, Issue 4, pp. 225-249, 1988.
[20] I. Titov and R. McDonald., A joint model of text and aspect ratings for sentiment summarization, Proceedings of the 46th Annual Meeting of the Association of Computational Linguistic, pp. 308-316, 2008.
[21] J. Trout, Positioning is a game people play in today’s me-too market place, Industrial Marketing, Volume 54, Issue 6, pp. 51-55, 1969.
[22] W. Vanlaar, H. Simpson and R. Robertson, A perceptual map for understanding concern about unsafe, Accident Analysis & Prevention, Volume 40, Issue 5, pp. 1667-1673, 2008.
[23] H. Wang, Y. Lu and C. Zha, Latent aspect rating analysis on review text data: A rating regression approach, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 783-792, 2010.
[24] Z. Zhai, B. Liu, H. Xu and P. Jia, Clustering product features for opinion mining, Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 347-354, 2011.
[25] Z. Zhai, B. Liu, H. Xu and P. Jia, Constrained LDA for grouping product features in opinion mining, Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 448-459, 2011.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61529-
dc.description.abstract顧客評論是公司珍貴的資源,因為顧客常分享他們的使用經驗並對各產品特徵提供有用的意見。因此,在這篇論文中,我們提出一個MPM(Mining Perceptual Map)的方法,自動地從顧客評論中建構知覺定位圖與雷達圖,知覺定位圖與雷達圖都是廣泛應用於行銷與商業分析中的視覺化工具。從大量的顧客評論中,建構知覺定位圖與雷達圖,可以降低個人主觀的偏見。本實驗結果可提供智慧型手機業者一些實用的觀察與建議,我們提出的方法可幫助公司定位其產品並制定有效的競爭策略。zh_TW
dc.description.abstractConsumer reviews are valuable resources for companies since consumers usually share their using experiences on the product or provide useful opinions from various aspects such as different product features. Therefore, in this thesis, we propose a method called MPM (Mining Perceptual Map) to automatically build perceptual maps and radar charts from consumer reviews. Perceptual maps and radar charts are business tools widely used in marketing and business analysis. The proposed method may reduce subjective personal bias since perceptual maps and radar charts are mined from a large number of consumer reviews. The experimental results may provide some practical insight for smartphone companies. Our method can help firms to position new products, and formulate effective marketing and competitive strategies.en
dc.description.provenanceMade available in DSpace on 2021-06-16T13:05:04Z (GMT). No. of bitstreams: 1
ntu-102-R00725002-1.pdf: 2012534 bytes, checksum: 70c472adc58509686f65832ea806853c (MD5)
Previous issue date: 2013
en
dc.description.tableofcontentsList of Figures vii
List of Tables viii
Chapter 1 Introduction 1
Chapter 2 Related Work 5
2.1 Clustering product features 5
2.2 Building perceptual maps and radar charts 7
2.3 Sentiment analysis 7
Chapter 3 Preliminary Concepts and Problem Definitions 9
Chapter 4 The Proposed Method 11
4.1 Extracting product features 11
4.2 Building a virtual document for each product feature 11
4.3 Clustering product features 13
4.4 Pruning redundant product features 15
4.5 Building perceptual maps and radar charts 16
Chapter 5 Experimental Results 18
5.1 Datasets 18
5.2 Performance of clustering product features 19
5.3 Perceptual map 22
5.3.1 By brand 22
5.3.2 By price 26
Chapter 6 Conclusions and Future Work 29
References 31
dc.language.isoen
dc.subject隱含狄利克雷分佈zh_TW
dc.subject意見探勘zh_TW
dc.subject情感分析zh_TW
dc.subject雷達圖zh_TW
dc.subject知覺定位圖zh_TW
dc.subjectsentiment analysisen
dc.subjectopinion miningen
dc.subjectLatent Dirichlet Allocationen
dc.subjectperceptual mapsen
dc.subjectradar chartsen
dc.title從顧客評論探勘知覺定位圖zh_TW
dc.titleMining Perceptual Maps from Consumer Reviewsen
dc.typeThesis
dc.date.schoolyear101-2
dc.description.degree碩士
dc.contributor.oralexamcommittee陳彥良,劉敦仁
dc.subject.keyword情感分析,意見探勘,隱含狄利克雷分佈,知覺定位圖,雷達圖,zh_TW
dc.subject.keywordsentiment analysis,opinion mining,Latent Dirichlet Allocation,perceptual maps,radar charts,en
dc.relation.page33
dc.rights.note有償授權
dc.date.accepted2013-08-05
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept資訊管理學研究所zh_TW
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