參考文獻 |
[1] Sernovitz, A., Word of Mouth Marketing., New York: Kaplan Publishing., 2009.
[2] Ott M, Choi Y J, Cardie C, et al. Finding Deceptive Opinion Spam by Any Stretch of the Imagination [C]. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2011: 309-319.
[3] Jindal N, Liu B. Review Spam Detection [C]. In: Proceedings of the 16th International Conference on World Wide Web. New York, NY, USA: ACM, 2007: 1189-1190.
[4] Mukherjee A, Venkataraman V. What Yelp Fake Review Filter Might Be Doing? [C]. In: Proceedings of the 7th International Conference on Weblogs and Social Media. Palo Alto: AAAI Press, 2013: 409-418.
[5] Wu G, Greene D, Smyth B, et al. Distortion as a Validation Criterion in the Identification of Suspicious Reviews[C]. In: Proceedings of the 1st Workshop on Social Media Analytics. New York, NY, USA: ACM, 2010: 10-13.
[6] 李霄, 丁晟春. 垃圾商品評論信息的識別研究[J]. 現代圖書情報技術, 2013(1): 63-68. (Li Xiao, Ding Shengchun. Research on Review Spam Recognition[J]. New Technology of Library and Information Service, 2013(1): 63-68.)
[7] 邱雲飛, 王建坤, 邵良杉, 等. 基於用戶行為的產品垃圾評論者檢測研究[J]. 計算機工程, 2012, 38(11): 254-257, 261. (Qiu Yunfei, Wang Jiankun, Shao Liangshan, et al. Research on Product Review Spammer Detection Based on Users’Behavior[J]. Computer Engineering, 2012, 38(11):254-257, 261.)
[8] 孫升芸, 田萱, 何軍. 基於評論行為的商品垃圾評論的識別研究[J]. 計算機工程與設計, 2012, 33(11): 4315-4319. (Sun Shengyun, Tian Xuan, He Jun. Research on Product Review Spam Detection Based on Review Behavior[J]. Computer Engineering and Design, 2012, 33(11): 4315- 4319.)
[9] 吳敏, 何瓏. 融合多特徵的產品垃圾評論識別[J]. 微型機與應用, 2012, 31(22): 85-87, 90. (Wu Min, He Long. Fuse Multi-features to Identify Product Review Spam[J]. Microcomputer & Its Applications, 2012, 31(22): 85-87, 90.)
[10] 陸軍, 洪宇, 陸劍江, 等. 基於全局用戶意圖的評論自動估價方法研究[J]. 中文信息學報, 2012, 26(5): 79-87. (Lu Jun, Hong Yu, Lu Jianjiang, et al. Automatic Reviews Quality Evaluation Based on Global User Intent[J]. Journal of Chinese Information Processing, 2012, 26(5): 79-87.)
[11] Dellarocas C. The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms [J]. Management science,2003,49( 10) : 1407-1424
[12] Chevalier J A,Mayzlin D. The Effect of Word of Mouth on Sales: Online Book Reviews [ J ] . Journal of Marketing Research,2006,43( 3) : 345 - 354;
[13] Cone Research. “Game changer: cone survey finds 4-out-of-5 consumers reverse purchase decisions based on negative online reviews”. Available at: http://www.conecomm.com/contentmgr/showdetails.php/id/4008.
[14] Michael Anderson, Jeremy Magruder. Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database 9 MAR 2012
[15] Opinion Research Corporation, Online consumer reviews significantly impact consumer purchasing decisions, 2008.06, http://www.opinionresearch.com/fileSave/Online_Feedback_PR_Final_6202 008 .pdf
[16] 資訊工業促進會產業情報研究所: 台灣網友線上購物行為調查http://ecommercetaiwan.blogspot.tw/2013/12/2013_4026.html
[17] Hu, Nan and Liu, Ling and Zhang, Jie (Jennifer), Do Online Reviews Affect Product Sales? The Role of Reviewer Characteristics and Temporal Effects (January 7,2008).
[18] Hu,N., Liu, L., and Sambamurthy, V., Fraud detection in online consumer reviews, Decision Support Systems, vol.50, pp.614-626, 2012.
[19] Kapferer, J. N., Rumors- Uses, Interpretations, and Images, New Brunswick: Transaction Publishers, 1990. 鄭若麟、邊芹譯,謠言,台北:桂冠圖書,民國 81 年。
[20] Knapp, R. H. “A Psychology of Rumor”, Public Opinion Quarterly, (53), 1944: pp. 467-481.
[21] Koenig, F., Rumor in the Marketplace, Dover: Auburn House, 1985.
[22] Shibutani, T., Improvised News: A Sociological Study of Rumor,Indianapolis: Bobbs Merrill, 1966.
[23] Allport, G. W. and Postman, L. “An Analysis of Rumor”, Public Opinion Quarterly, (10), 1947: pp. 501-517.
[24] Allport, G. W. and Postman, L., The Psychology of Rumor, New York: Henry Holt, 1947.
[25] Rownow, R. L., “On Rumor”, Journal of Communication, (24:3), 1974: pp.26-38.
[26] Jindal N, Liu Bing. Opinion spam and analysis[C]//Proceedings of the 1st ACM International Conference on Web Search and Data Mining (WSDM’08), California, USA, Feb 11-12, 2008. New York, NY, USA: ACM, 2008: 137-142.
[27] Sammons,M.C. (1999).The Internet Writer’s Handbook. Boston: MA: Allyn and Bacon.
[28] Nie Hui,Wang Jiajia. Review of Product Review Spams Detection, 2014
[29] Kusumasondjaja S, Shanka T, Marchegiani C. Credibility of Online Reviews and Initial Trust: The Roles of Reviewer’s Identity and Review Valence[J]. Journal of Vacation Marketing, 2012, 18(3): 185-195.
[30] Pan L Y, Chiou J S. How Much Can You Trust Online Information? Cues for Perceived Trustworthiness of Consumer-generated Online Information[J]. Journal of Interactive Marketing, 2011, 25(2): 67-74.
[31] Lim E P, Nguyen V A, Jindal N, et al. Detecting Product Review Spammers Using Rating Behaviors [C]. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2010: 939-948.
[32] W. J. Frawley, G. Piatetsky-Shapiro, and C. J. Matheus, Knowledge Discovery in Databases: An Overview, AI Magazine, Vol. 13, No. 3, pp.57-70, 1992.
[33] Berry, M. J. A. & Linoff, G., Data Mining Techniques: for Marking, Sales, and Customer Support. New York: John Wiley & Sons Inc., 1997.
[34] D. Hand, H. Mannila, P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge,MA. ISBN 0-262-08290-X.?
[35] Jiawei Han and Micheline Kamber,Data Mining: Concepts and Techniques, Simon Fraser University, Morgan Kaufmann Publishers,2001.
[36] Feldman R., Dagan I., “Knowledge discovery in textual databases (KDT).” Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), Montreal, Canada, 1995, AAAI Press, pp.112-117.
[37] Hearst M. A.,“Text data mining: Issues, techniques, and the relationship to information access.” Presentation notes for UW/MS workshop on data mining, 1997.
[38] Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R., From data mining to knowledge discovery: “An Overview. In Advances in Knowledge Discovery and Data Mining.” MIT Press, Cambridge, Mass., 1996, pp.1-36.
[39] Simoudis E., “Reality check for data mining.” 1996, IEEE Expert, (11:5)
[40] Tan, A.-H. (1999), “Text Mining: The state of the art and the challenges”, in Proceedings, PAKDD’99 workshop on Knowledge Discovery from Advanced Databases, Beijing, April, 1999.
[41] Losiewicz, P., Oard, D. W., & Kostoff, R. N. (2000). Textual data mining to support science and technology management. Online: http://www.onr.navy.mil/sci_tech/special/technowatch/textmine.htm
[42] 國家實驗研究院—專題企劃:提昇國家競爭力-科技政策研究與資訊服務http://www.narl.org.tw/tw/pressroom/topic/topic.php?group_id=21&topic_id=79
[43] Li Fangtao, Huang Minlie, Yang Yi, et al. Learning to identify review spam[C]//Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI ‘11), Bar celona, Spain, Jul 16-22, 2011. Palo Alto, CA, USA: AAAI, 2011: 2488-2493.
[44] Feng Song, Banerjee R, Choi Y. Syntactic stylometry for deception detection[C]//Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL’12), Jeju Island, Korea, Jul 8-14, 2012. Stroudsburg, PA, USA: ACL, 2012: 171-175.
[45] Jindal N, Liu B. Opinion Spam and Analysis[C]. In:Proceedings of the 2008 International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2008: 219-230.
[46] Lappas T. Fake reviews:The malicious perspective. Procedings of the 17th International conference on Applications of Natural Language Procesing to Information Systems,2012:23~34.
[47] 雷秉翰,許秉瑜,辨別含有可疑內容網路產品評論之研究(Identify the online product comments with suspicious content),2013
[48] Ott M,Choi Y,Caridie C, et al. Finding deceptive opinion spam by any stretch of the imagination[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies , Portland,USA,Jun 19-24,2011.
[49] Schultz C.K., H.P. Luhn: Pioneer of Information Science - Selected Works, Macmillan,. London,1968.
[50] REN Yafeng, YIN Lan, JI Donghong Deceptive Reviews Detection Based on Language Structure and Sentiment Polarity,2014
[51] Jindal N, Liu B. Analyzing and Detecting Review Spam [C]. In: Proceedings of the 7th International Conference on Data Mining. Washington, DC, USA: IEEE Computer Society, 2007: 547-552.
[52] Cortes, C. (1995) Prediction of Generalization Ability in Learning Machines., Ph.D. Thesis, Department of Computer Science, University of Rochester.
[53] LeCun, Y., Jackel, L. D., Bottou, L., Brunot, A., Cortes, C., Deker, J.S., Drucker, H., Guyon, I., Muller, U.A., Sackinger, E., Siard, P. and Vapnik, V., (1995)Comparison of Learning algorithms for handwritten recognition , International Conference on Artificial Neural Networks, Fogelman, F. and Gallinari, P. (Ed.), pp. 53-60.
[54] Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard and Chih-Jen Lin (2010). Training and testing low-degree polynomial data mappings via linear SVM. J. Machine Learning Research 11: 1471–1490
[55] 徐晶凝,現代漢語話語情態研究,北京:崑崙出版社,2007。
[56] 陳韻竹,現代漢語可能性副詞可能性排序之研究,碩士論文:國立台灣師範大學華語文教學研究所,2009。 |