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Volumn , Issue , 2005, Pages 2341-2346

Sentiment classification for chinese reviews: A comparison between SVM and semantic approaches

Author keywords

Customer review; Opinion analysis; Semantic orientation approach; Sentiment classification; Support vector machine

Indexed keywords

DECISION MAKING; INFORMATION RETRIEVAL; SEMANTICS; WORLD WIDE WEB;

EID: 28444455954     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (47)

References (17)
  • 4
    • 28444495831 scopus 로고    scopus 로고
    • Text categorization with support vector machines: Learning with many relevant features
    • University of Dortmund, Computer Science Department
    • Joachims Thorsten. Text categorization with support vector machines: Learning with many relevant features. Technical report, University of Dortmund, Computer Science Department, 1997
    • (1997) Technical Report
    • Thorsten, J.1
  • 5
    • 9444244198 scopus 로고    scopus 로고
    • Pennock. mining the peanut gallery: Opinion extraction and semantic classification of product reviews
    • Budapest, Hungary
    • Kushal Dave, Steve Lawrence, and David M, "Pennock. Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews", proceedings of 12th international conference on World Wide Web, Budapest, Hungary, 2003
    • (2003) Proceedings of 12th International Conference on World Wide Web
    • Dave, K.1    Lawrence, S.2    David, M.3
  • 7
    • 2442507763 scopus 로고    scopus 로고
    • Measuring praise and criticism: Inference of semantic orientation from association
    • Peter D, Turney, and Michael L. Littman, "Measuring Praise and Criticism: Inference of Semantic Orientation from Association", ACM Transactions on Information Systems, vol.21, pp.315-346, 2003
    • (2003) ACM Transactions on Information Systems , vol.21 , pp. 315-346
    • Peter, D.T.1    Littman, M.L.2
  • 9
    • 0003425660 scopus 로고    scopus 로고
    • Text categorization with support vector machines: Learning with many relevant features
    • Computer Science Department
    • Thorsten Joachims. "Text categorization with support vector machines: Learning with many relevant features", technical report, University of Dortmund, Computer Science Department, 1997.
    • (1997) Technical Report, University of Dortmund
    • Joachims, T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.