박사

빅데이터 관리를 위한 오피니언 감성사전 모델 설계 = Design of Opinion Sensitivity Dictionary Model for Big Data Management

서지훈 2015년
논문상세정보
' 빅데이터 관리를 위한 오피니언 감성사전 모델 설계 = Design of Opinion Sensitivity Dictionary Model for Big Data Management' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 감성사전
  • 분류기법
  • 빅데이터
  • 오피니언마이닝
  • 자연어처리
  • 텍스트 마이닝
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
4,867 0

0.0%

' 빅데이터 관리를 위한 오피니언 감성사전 모델 설계 = Design of Opinion Sensitivity Dictionary Model for Big Data Management' 의 참고문헌

  • 주식 투자자의 의사결정 지원을 위한 데이터마이닝 도구
    김성동 한국콘텐츠학회논문지, 12(2), pp.472-482 [2012]
  • 온라인 쇼핑몰의 상품평 자동분류를 위한 감성분석 알고리즘
    장재영 한국전자거래학회, 한국전자거래학회지, 14(4), pp.13-33 [2009]
  • 빅데이터의 동향 및 시사점
    김한나 정보통신방송통신정책 제24권 19호, 대한민국 [2012]
  • 빅데이터 분석 기술의 오늘과 미래
    강만모 김상락 한국정보과학회,정보과학회지, 32(1), pp.8-17 [2014]
  • “한글 텍스트의 오피니언 분류 자동화 기법”
    김진옥 용환승 이선숙 정보과학회논문지 데이터베이스, 38(6), pp.423-428
  • “하둡과 맵리듀스”
    강다현 박정혁 원중호 이상열 한국데이터정보과학회지, 24(5), pp.1013-1027 [2013]
  • “하둡 모델의 분석 및 보완 연구”
    김수경 이진우 한국컴퓨터정보학회 학술발표대회자료, 대한민국, pp.3-6 [2012]
  • “전자메일 분류를 위한 나이브 베이지안 학습과 중심점179기반 분류의 성능 비교”
    권영식 김국표 대한산업공학회지, 18(1), pp.10-21 [2005]
  • “오피니언마이닝을 이용한 지능형 VOC 분석 시스템”
    김유신 정승렬 한국지능정보시스템학회, 지능정보연구, 19(3), pp.113-125 [2013]
  • “오피니언 마이닝에서의 텍스트 신뢰도 측정방법”
    김이준 임지연 한국정보과학회 한국컴퓨터종합학술대회, 38(1), pp.135-138 [2011]
  • “연관규칙 기반의 특허문서 분류 자동화 시스템”
    김성범 손지은 대한산업공학회 춘계학술대회 논문집, 대한민국, pp.575-586 [2013]
  • “신문기사로부터 추출한 최근동향에 대한 트위터 감성분석”
    이경호 이공주 정보처리학회논문지 소프트웨어 및 데이터공학, 2(10), pp.731-738.178 [2013]
  • “상품리뷰 요약에서의 문맥 정보를 이용한 의견분류 방법”
    명재석 양정연 이상구 한국정보과학회, 정보과학회논문지, 36(4), pp.254-262 [2009]
  • “상용 데이터 마이닝 도구를 사용한 정량적 연관규칙 마이닝”
    강공미 김진호 문양세 최훈영 정보과학회논문지 데이터베이스, 35(2), pp.97-111 [2008]
  • “빅데이터 분석을 위한 맵리듀스 알고리즘의 최근 영구동향”
    김영훈 김진현 박윤재 심규석 이정훈 한국정보과학회지, 32(1), pp.27-32 [2014]
  • “데이터 마이닝에서 비트 클러스트링을 이용한 향상된FP-Growth
    김의찬 황병언 정보과학회논문지 데이터베이스, 38(5), pp.280-288 [2011]
  • “개체연관망 모델에 의한 오피니언마이닝의 확장”
    김근형 정보처리학회논문지, 18D(4), pp.237-244 [2011]
  • “News Big Data Opinion Mining Model for Predicting KOSPIMovement”
    김유신 박사학위논문, 국민대학교, 대한민국 [2012]
  • 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.
  • Fabrizio Sebastiani, 2002, “Machine learning in automated textcategorization”, Journal ACM Computing Surveys, 34(1), pp.1-47.
  • 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.