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Volumn , Issue , 2010, Pages 1089-1094

United we stand: Improving sentiment analysis by joining machine learning and rule based methods

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; SPEECH PROCESSING;

EID: 84962791085     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (13)

References (13)
  • 1
    • 84859923482 scopus 로고    scopus 로고
    • When specialists and generalists work together: Overcoming domain dependence in sentiment tagging
    • 2008
    • A. Andreevskaia and S. Bergler. 2008. When specialists and generalists work together: overcoming domain dependence in sentiment tagging. In Proceedings of ACL-08: HLT, pages:290-298, 2008.
    • (2008) Proceedings of ACL-08: HLT , pp. 290-298
    • Andreevskaia, A.1    Bergler, S.2
  • 2
    • 80053377187 scopus 로고    scopus 로고
    • Learning with compositional semantics as structural inference for subsentential sentiment analysis
    • 2008, Association for Computational Linguistics
    • Y. Choi and C Cardie. 2008. Learning with compositional semantics as structural inference for subsentential sentiment analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 793-801, 2008, Association for Computational Linguistics.
    • (2008) Proceedings of the Conference on Empirical Methods in Natural Language Processing , pp. 793-801
    • Choi, Y.1    Cardie, C.2
  • 13
    • 80053247760 scopus 로고    scopus 로고
    • Recognizing contextual polarity in phrase-level sentiment analysis
    • Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In HLT/EMNLP.
    • (2005) HLT/EMNLP
    • Wilson, T.1    Wiebe, J.2    Hoffmann, P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.