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Volumn , Issue , 2014, Pages 259-264

ECNU: Expression- And Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective Features

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; SEMANTICS; SENTIMENT ANALYSIS; SUPPORT VECTOR MACHINES;

EID: 85045444908     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (16)

References (17)
  • 3
    • 85034054192 scopus 로고    scopus 로고
    • Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining
    • Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In LREC, volume 10, pages 2200–2204.
    • (2010) LREC , vol.10 , pp. 2200-2204
    • Baccianella, Stefano1    Esuli, Andrea2    Sebastiani, Fabrizio3
  • 6
    • 84930955825 scopus 로고    scopus 로고
    • Isti@ trec microblog track 2011: Exploring the use of hashtag segmentation and text quality ranking
    • Giacomo Berardi, Andrea Esuli, Diego Marcheggiani, and Fabrizio Sebastiani. 2011. Isti@ trec microblog track 2011: Exploring the use of hashtag segmentation and text quality ranking. In TREC.
    • (2011) TREC
    • Berardi, Giacomo1    Esuli, Andrea2    Marcheggiani, Diego3    Sebastiani, Fabrizio4
  • 15
    • 70349529656 scopus 로고    scopus 로고
    • Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
    • Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2009. Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational linguistics, pages 399–433.
    • (2009) Computational linguistics , pp. 399-433
    • Wilson, Theresa1    Wiebe, Janyce2    Hoffmann, Paul3
  • 17
    • 33745456231 scopus 로고    scopus 로고
    • Technical Report 1530, Computer Sciences, University of Wisconsin-Madison
    • Xiaojin Zhu. 2005. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison.
    • (2005) Semi-supervised learning literature survey
    • Zhu, Xiaojin1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.