메뉴 건너뛰기




Volumn 1, Issue , 2014, Pages 1252-1261

Opinion mining on YouTube

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; DATA MINING; SENTIMENT ANALYSIS; SYNTACTICS;

EID: 84906925179     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/p14-1118     Document Type: Conference Paper
Times cited : (39)

References (29)
  • 1
    • 57349126313 scopus 로고    scopus 로고
    • Inter-coder agreement for computational linguistics
    • December
    • Ron Artstein and Massimo Poesio. 2008. Inter-coder agreement for computational linguistics. Computational Linguistics, 34(4):555-596, December.
    • (2008) Computational Linguistics , vol.34 , Issue.4 , pp. 555-596
    • Artstein, R.1    Poesio, M.2
  • 2
    • 85121726203 scopus 로고    scopus 로고
    • How noisy social media text, how diffrnt social media sources?
    • Timothy Baldwin, Paul Cook, Marco Lui, Andrew MacKinlay, and Li Wang. 2013. How noisy social media text, how diffrnt social media sources? In IJCNLP.
    • (2013) IJCNLP
    • Baldwin, T.1    Cook, P.2    Lui, M.3    Mackinlay, A.4    Wang, L.5
  • 3
    • 84860524227 scopus 로고    scopus 로고
    • Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
    • John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL.
    • (2007) ACL
    • Blitzer, J.1    Dredze, M.2    Pereira, F.3
  • 4
    • 84860513476 scopus 로고    scopus 로고
    • Frustratingly easy domain adaptation
    • Hal Daumé, III. 2007. Frustratingly easy domain adaptation. ACL.
    • (2007) ACL
    • Daumé III, H.1
  • 6
    • 12244305149 scopus 로고    scopus 로고
    • Mining and summarizing customer reviews
    • Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In KDD.
    • (2004) KDD
    • Hu, M.1    Liu, B.2
  • 7
    • 0242456822 scopus 로고    scopus 로고
    • Optimizing search engines using clickthrough data
    • Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. In KDD.
    • (2002) KDD
    • Joachims, T.1
  • 8
    • 84907370061 scopus 로고    scopus 로고
    • The 2010 icwsm jdpa sentiment corpus for the automotive domain
    • Jason S. Kessler, Miriam Eckert, Lyndsie Clark, and Nicolas Nicolov. 2010. The 2010 ICWSM JDPA sentiment corpus for the automotive domain. In ICWSM-DWC.
    • (2010) ICWSM-DWC
    • Kessler, J.S.1    Eckert, M.2    Clark, L.3    Nicolov, N.4
  • 10
    • 34547975798 scopus 로고    scopus 로고
    • Efficient convolution kernels for dependency and constituent syntactic trees
    • Alessandro Moschitti. 2006a. Efficient convolution kernels for dependency and constituent syntactic trees. In ECML.
    • (2006) ECML
    • Moschitti, A.1
  • 11
    • 84874714333 scopus 로고    scopus 로고
    • Making tree kernels practical for natural language learning
    • Alessandro Moschitti. 2006b. Making tree kernels practical for natural language learning. In EACL, pages 113-120.
    • (2006) EACL , pp. 113-120
    • Moschitti, A.1
  • 12
    • 70349257998 scopus 로고    scopus 로고
    • Kernel methods, syntax and semantics for relational text categorization
    • Alessandro Moschitti. 2008. Kernel methods, syntax and semantics for relational text categorization. In CIKM.
    • (2008) CIKM
    • Moschitti, A.1
  • 14
    • 85028156346 scopus 로고    scopus 로고
    • Twitter as a corpus for sentiment analysis and opinion mining
    • Alexander Pak and Patrick Paroubek. 2010. Twitter as a corpus for sentiment analysis and opinion mining. In LREC.
    • (2010) LREC
    • Pak, A.1    Paroubek, P.2
  • 17
    • 84907345507 scopus 로고    scopus 로고
    • Embedding semantic similarity in tree kernels for domain adaptation of relation extraction
    • Barbara Plank and Alessandro Moschitti. 2013. Embedding semantic similarity in tree kernels for domain adaptation of relation extraction. In ACL.
    • (2013) ACL
    • Plank, B.1    Moschitti, A.2
  • 18
    • 84867477568 scopus 로고    scopus 로고
    • Named entity recognition in tweets: An experimental study
    • Alan Ritter, Sam Clark, Mausam, and Oren Etzioni. 2011. Named entity recognition in tweets: an experimental study. In ACL.
    • (2011) ACL
    • Ritter, A.1    Clark Mausam, S.2    Etzioni, O.3
  • 19
    • 84866604545 scopus 로고    scopus 로고
    • Structural relationships for large-scale learning of answer re-ranking
    • Aliaksei Severyn and Alessandro Moschitti. 2012. Structural relationships for large-scale learning of answer re-ranking. In SIGIR.
    • (2012) SIGIR
    • Severyn, A.1    Moschitti, A.2
  • 20
    • 84906925685 scopus 로고    scopus 로고
    • Automatic feature engineering for answer selection and extraction
    • Aliaksei Severyn and Alessandro Moschitti. 2013. Automatic feature engineering for answer selection and extraction. In EMNLP.
    • (2013) EMNLP
    • Severyn, A.1    Moschitti, A.2
  • 21
    • 84907313623 scopus 로고    scopus 로고
    • Learning semantic textual similarity with structural representations
    • Aliaksei Severyn, Massimo Nicosia, and Alessandro Moschitti. 2013. Learning semantic textual similarity with structural representations. In ACL.
    • (2013) ACL
    • Severyn, A.1    Nicosia, M.2    Moschitti, A.3
  • 23
    • 77954567494 scopus 로고    scopus 로고
    • How useful are your comments?: Analyzing and predicting youtube comments and comment ratings
    • Stefan Siersdorfer, Sergiu Chelaru, Wolfgang Nejdl, and Jose San Pedro. 2010. How useful are your comments?: Analyzing and predicting YouTube comments and comment ratings. In WWW.
    • (2010) WWW
    • Siersdorfer, S.1    Chelaru, S.2    Nejdl, W.3    San Pedro, J.4
  • 24
    • 80053261327 scopus 로고    scopus 로고
    • Semi-supervised recursive autoencoders for predicting sentiment distributions
    • Richard Socher, Jeffrey Pennington, Eric H Huang, Andrew Y Ng, and Christopher D Manning. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In EMNLP.
    • (2011) EMNLP
    • Socher, R.1    Pennington, J.2    Huang, E.H.3    Ng, A.Y.4    Manning, C.D.5
  • 25
    • 84906931749 scopus 로고    scopus 로고
    • Robust learning in random subspaces: Equipping nlp for oov effects
    • Anders Søgaard and Anders Johannsen. 2012. Robust learning in random subspaces: Equipping nlp for oov effects. In COLING.
    • (2012) COLING
    • Søgaard, A.1    Johannsen, A.2
  • 26
    • 84859032967 scopus 로고    scopus 로고
    • Semisupervised latent variable models for sentence-level sentiment analysis
    • Oscar Täckström and Ryan McDonald. 2011. Semisupervised latent variable models for sentence-level sentiment analysis. In ACL.
    • (2011) ACL
    • Täckström, O.1    McDonald, R.2
  • 27
    • 85020939189 scopus 로고    scopus 로고
    • SenTube: A corpus for sentiment analysis on youtube social media
    • Olga Uryupina, Barbara Plank, Aliaksei Severyn, Agata Rotondi, and Alessandro Moschitti. 2014. SenTube: A corpus for sentiment analysis on YouTube social media. In LREC.
    • (2014) LREC
    • Uryupina, O.1    Plank, B.2    Severyn, A.3    Rotondi, A.4    Moschitti, A.5
  • 28
    • 84875872773 scopus 로고    scopus 로고
    • Baselines and bigrams: Simple, good sentiment and topic classification
    • Sida Wang and Christopher Manning. 2012. Baselines and bigrams: Simple, good sentiment and topic classification. In ACL.
    • (2012) ACL
    • Wang, S.1    Manning, C.2
  • 29
    • 80053247760 scopus 로고    scopus 로고
    • Recognizing contextual polarity in phraselevel sentiment analysis
    • Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phraselevel sentiment analysis. In EMNLP.
    • (2005) EMNLP
    • Wilson, T.1    Wiebe, J.2    Hoffmann, P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.