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Volumn , Issue , 2015, Pages 252-257

Using Ensemble Learners to Improve Classifier Performance on Tweet Sentiment Data

Author keywords

Bagging; Boosting; classification; sentiment analysis; tweet mining

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; INFORMATION USE;

EID: 84959129790     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IRI.2015.49     Document Type: Conference Paper
Times cited : (44)

References (21)
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  • 3
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    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 4
    • 80053403826 scopus 로고    scopus 로고
    • Ensemble methods in machine learning
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    • T. G. Dietterich, "Ensemble methods in machine learning," in Multiple classifier systems. Springer, 2000, pp. 1-15.
    • (2000) Multiple Classifier Systems , pp. 1-15
    • Dietterich, T.G.1
  • 12
    • 85128719106 scopus 로고    scopus 로고
    • Twitter sentiment analysis: The good the bad and the omg!
    • E. Kouloumpis, T. Wilson, and J. Moore, "Twitter sentiment analysis: The good the bad and the omg!" ICWSM, vol. 11, pp. 538-541, 2011.
    • (2011) ICWSM , vol.11 , pp. 538-541
    • Kouloumpis, E.1    Wilson, T.2    Moore, J.3
  • 14
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    • Meador, C.1    Gluck, J.2
  • 16
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    • The case against accuracy estimation for comparing induction algorithms
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    • Provost, F.J.1    Fawcett, T.2    Kohavi, R.3
  • 20
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    • Learning when training data are costly: The effect of class distribution on tree induction
    • G. M. Weiss and F. J. Provost, "Learning when training data are costly: the effect of class distribution on tree induction," J. Artif. Intell. Res.(JAIR), vol. 19, pp. 315-354, 2003.
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    • Weiss, G.M.1    Provost, F.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.