메뉴 건너뛰기




Volumn , Issue , 2011, Pages 44-52

Developing Robust Models for Favourability Analysis

Author keywords

[No Author keywords available]

Indexed keywords

BALANCING; CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; MACHINE LEARNING;

EID: 85083983543     PISSN: 0736587X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (12)

References (23)
  • 2
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • A.L. Blum and P. Langley. 1997. Selection of relevant features and examples in machine learning. Artificial intelligence, 97:245-271.
    • (1997) Artificial intelligence , vol.97 , pp. 245-271
    • Blum, A.L.1    Langley, P.2
  • 4
    • 2942731012 scopus 로고    scopus 로고
    • An extensive empirical study of feature selection metrics for text classification
    • G. Forman. 2003. An extensive empirical study of feature selection metrics for text classification. The Journal of Machine Learning Research, 3:1289-1305.
    • (2003) The Journal of Machine Learning Research , vol.3 , pp. 1289-1305
    • Forman, G.1
  • 8
    • 33646890004 scopus 로고    scopus 로고
    • The importance of neutral examples for learning sentiment
    • M. Koppel and J. Schler. 2006. The importance of neutral examples for learning sentiment. Computational Intelligence, 22:100-109.
    • (2006) Computational Intelligence , vol.22 , pp. 100-109
    • Koppel, M.1    Schler, J.2
  • 10
    • 0031998121 scopus 로고    scopus 로고
    • Machine learning for the detection of oil spills in satellite radar images
    • M. Kubat, R.C. Holte, and S. Matwin. 1998. Machine learning for the detection of oil spills in satellite radar images. Machine Learning, 30:195-215.
    • (1998) Machine Learning , vol.30 , pp. 195-215
    • Kubat, M.1    Holte, R.C.2    Matwin, S.3
  • 14
    • 84957081878 scopus 로고    scopus 로고
    • Feature subset selection in textlearning
    • D. Mladeníc. 1998. Feature subset selection in textlearning. Machine Learning: ECML-98, pages 95- 100.
    • (1998) Machine Learning: ECML-98 , pp. 95-100
    • Mladeníc, D.1
  • 15
    • 85112593657 scopus 로고    scopus 로고
    • Sentiment analysis using support vector machines with diverse information sources
    • T. Mullen and N. Collier. 2004. Sentiment analysis using support vector machines with diverse information sources. In Proceedings of EMNLP, volume 4, pages 412-418.
    • (2004) Proceedings of EMNLP , vol.4 , pp. 412-418
    • Mullen, T.1    Collier, N.2
  • 18
    • 62349089279 scopus 로고    scopus 로고
    • Sentiment analysis: A combined approach
    • R. Prabowo and M. Thelwall. 2009. Sentiment analysis: A combined approach. Journal of Informetrics, 3:143-157.
    • (2009) Journal of Informetrics , vol.3 , pp. 143-157
    • Prabowo, R.1    Thelwall, M.2
  • 20
    • 84881506644 scopus 로고    scopus 로고
    • Aggregating opinions: Explorations into Graphs and Media Content Analysis
    • G. Tatzl and C. Waldhauser. 2010. Aggregating opinions: Explorations into Graphs and Media Content Analysis. ACL 2010, page 93.
    • (2010) ACL 2010 , pp. 93
    • Tatzl, G.1    Waldhauser, C.2
  • 21
    • 85136072040 scopus 로고    scopus 로고
    • Thumbs up or thumbs down: Semantic orientation applied to unsupervised classification of reviews
    • Association for Computational Linguistics
    • P.D. Turney. 2002. Thumbs up or thumbs down: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pages 417-424. Association for Computational Linguistics.
    • (2002) Proceedings of the 40th Annual Meeting on Association for Computational Linguistics , pp. 417-424
    • Turney, P.D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.