Zijian Zheng, Ron Kohavi and Llew Mason, 2001, “Real world performance of176association rule algorithms”, Proceedings of the seventh ACM SIGKDDinternational conference on Knowledge discovery and data mining, New York,USA, pp.401-406.
ZHU Jian, XU Chen and WANG Han-shi, 2010, “Sentiment classification using174the theory of ANNs”, The Journal of China Universities of Posts andTelecommunications, Vol.17, pp.58-62.
Yong Qiu, Yong-Jie Lan and Qing-Song Xie, 2004, “An improved algorithmof mining from FP-tree”, Proceedings of 2004 International Conference onMachine Learning and Cybernetics, Shanghai, China, pp.26-29.
V Sindhwani and P Melville, 2008, “Document-Word Co-regularization forSemi-supervised Sentiment Analysis”, 8th IEEE International Conference onData Mining, Pisa, Italy, pp.1025-1030.
Theresa Wilson, Janyce Wiebe and Paul Hoffmann, 2005, “Recognizingcontextual polarity in phrase-level sentiment analysis”, Proceedings of theconference on Human Language Technology and Empirical Methods in NaturalLanguage Processing, PA, USA, pp.347-354.
Tata, Sandeep and Jignesh M. Patel, 2007, “Estimating the selectivity oftf-idf based cosine similarity predicates”, ACM SIGMOD Record, 36(2),pp.7-23.
T White, 2009, “Hadoop : the definitive guide”, O'REILLY, USA.
T Kanungo, et al., 2002, “An efficient k-means clustering algorithm: Analysisand implementation”, IEEE Transactions on Pattern Analysis and MachineIntelligence, 24(7), pp.881-892.
T Joachims, 1998, “Text categorization with support vector machines:Learning with many relevant features”, Proceedings of the 10th Europeanconference on mNeighbor-weighted k-nearest neighbor for unbalanced textcorpusachine learning, New York, USA, pp.137-142.
T Jo, M Lee and TM Gatton, 2006, “Keyword Extraction from DocumentsUsing a Neural Network Model”, International Conference on HybridInformation Technology, Jeju Island, KOREA, pp.194-197.
Suykens, Johan AK and Joos Vandewalle, 1999, “Least squares support vectormachine classifiers”, Neural Processing Letters , 9(3), pp.293-300.
Sunita Sarawagi, Shiby Thomas and Rakesh Agrawal, 1998, “Integratingassociation rule mining with relational database systems: alternatives andimplications”, Proceedings of the 1998 ACM SIGMOD international conferenceon Management of data, New York, USA, pp.343-354.177
Shvachko, Konstantin, et al., 2010, “The hadoop distributed file system”,2010 IEEE 26th Symposium on MSST, Incline Village, USA, pp.1-10.
S Tan, 2005, “Neighbor-weighted k-nearest neighbor for unbalanced textcorpus”, Expert Systems with Applications, 28(4), pp.667-671.
S Soderland, 1999, “Learning information extraction rules for semi-structuredand free text”, Machine learning, 34(1-3), pp.233-272.
S Soderland, 1997, “Learning to Extract Text-Based Information from theWorld Wide Web”, KDD, Vol. 97, pp.251-254.
Rudy Prabowo and Mike Thelwall, 2009, “Sentiment analysis: A combinedapproach”, Journal of Informetrics, 3(2), pp.143-157.
Ratanamahatana and Chotirat, 2003, “Feature selection for the naivebayesian classifier using decision trees”, Applied Artificial Intelligence,17(5-6), pp.475-487.
Ramanathan Narayanan, Bing Liu and Alok Choudhary, 2009, “Sentimentanalysis of conditional sentences”, Proceedings of the 2009 Conference onEmpirical Methods in Natural Language Processing, Singapore, pp.180-189.
Peter D. Turney, 2002, “Thumbs up or thumbs down?: semantic orientationapplied to unsupervised classification of reviews”, Proceedings of the 40thAnnual Meeting on Association for Computational Linguistics, Philadelphia,USA, pp.417-424.
Pang and L. Lee, 2008, “Opinion Mining and Sentiment Analysis”,172Foundation and Trends in Information Retrieval, 2(1-2), pp.1-135.
Mobasher, Bamshad, et al., 2001, “Effective personalization based onassociation rule discovery from web usage data”, Proceedings of the 3rdinternational workshop on Web information and data management, New York,USA, pp.401-406.
Mary Elaine Califf and Raymond J. Mooney, 1999, “Relational learning ofpattern-match rules for information extraction”, AAAI Technical Report,pp.328-334.
Mark Hall, Eibe Frank, el al., 2009, “The WEKA data mining software: anupdate”, ACM SIGKDD Explorations Newsletter, 11(1), pp.10-18.
Manovich, 2011, “Trending : The Promises and the Challenges of Big SocialData”, Debates in the Digital Humanities, America.
Jeonghee Yi and Wayne Niblack, 2005, “Sentiment mining in WebFountain”,Proceeding of the 21st International Conference on Data Engineering,California, USA, pp.1073-1083.
James Manyuka, et al., 2011, “Big Data : The Next Frontier for Innovation,Competition, and Productivity”, McKinsey Global Institute, America.
J. Chen, H. Huang, S. Tian and Y. Qu, 2009, “Feature selection for textclassification with Naive Bayes”, Expert Systems with Applications, 36(3),pp.5432-5435.
Herodotos Herodotou, Harold Lim, Gang Luo, Nedvalko Borisov and LiangDong, 2011, “Starfish: A Selftuning System for Big Data Analytics”, CIDR,Vol. 11, pp.261-272.
Han, Eui-Hong Sam, and George Karypi, 2000, “Centroid-based documentclassification: Analysis and experimental results”, Principles of Data Miningand Knowledge Discovery Lecture Notes in Computer Science, Vol.1910,pp.424-431.
Ghose, P. G. Ipeirotis and A. Sundararajan, 2007, “Opinion Mining UsingEconometrics : A Case Study on Reputation System” Proceedings of the 45thAnnual Meeting of the Association of Computational Linguistics, Prague, CzechRepublic, pp.416-423.
E. Courses and T, Surveys, 2008, “Using Sentiment SentiWordNet formultilingual sentiment analysis”, IEEE 24th International Conference on DataEngineering Workshop 2008, Cancun, Mexico, pp.507-512.
E Frank and RR Bouckaert, 2006, “Naive Bayes for text classification withunbalanced classes”, Proceedings of the 10th European conference onprinciples and practice of knowledge discovery in databases, Berlin, Germany,175pp.503-510.
Dong Li, Anne Laurent, Mathieu Roche, and Pascal Poncelet, 2008,“Extraction of Opposite Sentiments in Classified Free Format Text Reviews”,Proceedings of the 19th International Conference on Database and ExpertSystems Applications, Turin, Italy, pp.710-717.
Dayne Freitag, 1998, “Information extraction from HTML: Application of ageneral machine learning approach”, AAAI Technical Report, pp.517-523.
DENG Bo, FAN Xiao-zhong and YANG Li-gong, 2006, “A method of textclassification based on statistical technology and set theory”, Transactions ofBeijing Institute of Technology, 26(27), pp.589-597.
Chen and D. Zimbra, 2010, “AI and Opinion Mining”, IEEE IntelligentSystems, 25(3), pp.74-80.
Chang, Chia Hui, et al., 2006, “A survey of web information extractionsystems”, IEEE Transactions on Knowledge and Data Engineering, 18(10),pp.1411-1428.
Bo pang, Lillian Lee and Shivakumar Vaithyanathan, 2002, “Thumbs up?:sentiment classification using machine learning techniques”, Proceedings ofthe ACL-02 Conference on Empirical methods in Natural Language Processing,Vol.10, pp.79-86.
Bing Liu, Minqing Hu and Junsheng Cheng, 2005, “Opinion observer:analyzing and comparing opinions on the Web”, Proceedings of the 14thInternational Conference on World Wide Web, New York, USA, pp.342-351.
Bing Liu and Wynne Hsu, 1996, “Post-analysis of learned rules”,Proceedings of the thirteenth national conference on Artificial intelligence,Vol.1, pp.828-834.
Banko, Michele, et al., 2007, “Open information extraction for the web”,IJCAI, Vol. 7, pp.2670-2676.
Alex J. Smola and Bernhard Scholkopf, 2004, “A tutorial on support vectorregression”, Statistics and Computing, 14(3), pp.199-222.
Agrawal, Rakesh, et al., 1996, “Fast Discovery of Association Rules”,Advances in knowledge discovery and data mining, 12(1), p.307-328.
A. Mittermayer and G. F Knolmayer, 2006, “Text Mining Systems for MarketResponse to News: A Survey”, The Institute of Information Systems,University of Bern, Switzerland.
?[1] Stonebraker, M., 2010, “SQL Databases v. NoSQL Databases”, Communicationsof the ACM, 53(4), pp.10-11.
'
빅데이터 관리를 위한 오피니언 감성사전 모델 설계 = Design of Opinion Sensitivity Dictionary Model for Big Data Management'
의 유사주제(
) 논문