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Volumn , Issue , 2011, Pages 538-541

Twitter Sentiment Analysis: The Good the Bad and the OMG!

Author keywords

[No Author keywords available]

Indexed keywords

LINGUISTICS; SOCIAL NETWORKING (ONLINE);

EID: 85128719106     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (930)

References (15)
  • 1
    • 80053225960 scopus 로고    scopus 로고
    • Robust sentiment detection on twitter from biased and noisy data
    • Barbosa, L., and Feng, J. 2010. Robust sentiment detection on twitter from biased and noisy data. In Proc. of Coling.
    • (2010) Proc. of Coling.
    • Barbosa, L.1    Feng, J.2
  • 3
    • 84952634756 scopus 로고    scopus 로고
    • Enhanced sentiment learning using twitter hashtags and smileys
    • Davidov, D.; Tsur, O.; and Rappoport, A. 2010. Enhanced sentiment learning using twitter hashtags and smileys. In Proceedings of Coling.
    • (2010) Proceedings of Coling
    • Davidov, D.1    Tsur, O.2    Rappoport, A.3
  • 4
    • 85031789656 scopus 로고    scopus 로고
    • SentiWordNet: A publicly available lexical resource for opinion mining
    • Esuli, A., and Sebastiani, F. 2006. SentiWordNet: A publicly available lexical resource for opinion mining. In Proceedings of LREC.
    • (2006) Proceedings of LREC
    • Esuli, A.1    Sebastiani, F.2
  • 5
    • 85121752948 scopus 로고    scopus 로고
    • Predicting the semantic orientation of adjectives
    • Hatzivassiloglou, V., and McKeown, K. 1997. Predicting the semantic orientation of adjectives. In Proc. of ACL.
    • (1997) Proc. of ACL.
    • Hatzivassiloglou, V.1    McKeown, K.2
  • 9
    • 85028156346 scopus 로고    scopus 로고
    • Twitter as a corpus for sentiment analysis and opinion mining
    • Pak, A., and Paroubek, P. 2010. Twitter as a corpus for sentiment analysis and opinion mining. In Proc. of LREC.
    • (2010) Proc. of LREC.
    • Pak, A.1    Paroubek, P.2
  • 11
    • 0033905095 scopus 로고    scopus 로고
    • BoosTexter: A boosting-based system for text categorization
    • (2/3)
    • Schapire, R. E., and Singer, Y. 2000. BoosTexter: A boosting-based system for text categorization. Machine Learning 39(2/3):135–168.
    • (2000) Machine Learning , vol.39 , pp. 135-168
    • Schapire, R. E.1    Singer, Y.2
  • 12
    • 84890668120 scopus 로고    scopus 로고
    • Predicting elections with twitter: What 140 characters reveal about political sentiment
    • Tumasjan, A.; Sprenger, T. O.; Sandner, P.; and Welpe, I. 2010. Predicting elections with twitter: What 140 characters reveal about political sentiment. In Proceedings of ICWSM.
    • (2010) Proceedings of ICWSM
    • Tumasjan, A.1    Sprenger, T. O.2    Sandner, P.3    Welpe, I.4
  • 14
    • 70349529656 scopus 로고    scopus 로고
    • Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
    • Wilson, T.; Wiebe, J.; and Hoffmann, P. 2009. Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics 35(3):399–433.
    • (2009) Computational Linguistics , vol.35 , Issue.3 , pp. 399-433
    • Wilson, T.1    Wiebe, J.2    Hoffmann, P.3
  • 15
    • 85125365322 scopus 로고    scopus 로고
    • Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences
    • Yu, H., and Hatzivassiloglou, V. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proc. of EMNLP.
    • (2003) Proc. of EMNLP.
    • Yu, H.1    Hatzivassiloglou, V.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.