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Volumn , Issue , 2011, Pages 81-87

Sentiment analysis of citations using sentence structure-based features

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC IDENTIFICATION; DEPENDENCY RELATION; LEXICAL FEATURES; LINGUISTIC DIFFERENCES; N-GRAMS; NEGATIVE SENTIMENTS; SCIENTIFIC PAPERS; SCIENTIFIC TEXTS; SENTIMENT ANALYSIS; STRUCTURE-BASED;

EID: 84859097823     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (171)

References (41)
  • 2
    • 84860524227 scopus 로고    scopus 로고
    • Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
    • J. Blitzer, M. Dredze, and F. Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL, volume 45, page 440.
    • (2007) ACL , vol.45 , pp. 440
    • Blitzer, J.1    Dredze, M.2    Pereira, F.3
  • 3
    • 0020153770 scopus 로고
    • Characteristics of a literature as predictors of relatedness between cited and citing works
    • S. Bonzi. 1982. Characteristics of a literature as predictors of relatedness between cited and citing works. Journal of the American Society for Information Science, 33(4):208-216. (Pubitemid 13590734)
    • (1982) Journal of the American Society for Information Science , vol.33 , Issue.4 , pp. 208-216
    • Bonzi Susan1
  • 5
    • 34249753618 scopus 로고
    • Support-vector networks
    • C. Cortes and V. Vapnik. 1995. Support-vector networks. Machine learning, 20(3):273-297.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 8
    • 70549102500 scopus 로고    scopus 로고
    • The Stanford typed dependencies representation
    • Association for Computational Linguistics
    • M.C. de Marneffe and C.D. Manning. 2008. The Stanford typed dependencies representation. In COLING, pages 1-8. Association for Computational Linguistics.
    • (2008) Coling , pp. 1-8
    • De Marneffe, M.C.1    Manning, C.D.2
  • 13
    • 80053347311 scopus 로고    scopus 로고
    • Studying the history of ideas using topic models
    • D. Hall, D. Jurafsky, and C.D. Manning. 2008. Studying the history of ideas using topic models. In EMNLP, pages 363-371.
    • (2008) EMNLP , pp. 363-371
    • Hall, D.1    Jurafsky, D.2    Manning, C.D.3
  • 14
    • 85121752948 scopus 로고    scopus 로고
    • Predicting the semantic orientation of adjectives
    • Association for Computational Linguistics
    • V. Hatzivassiloglou and K.R. McKeown. 1997. Predicting the semantic orientation of adjectives. In Proceedings of EACL, pages 174-181. Association for Computational Linguistics.
    • (1997) Proceedings of EACL , pp. 174-181
    • Hatzivassiloglou, V.1    McKeown, K.R.2
  • 18
    • 84974295346 scopus 로고
    • Technical terminology: Some linguistic properties and an algorithm for identification in text
    • J.S. Justeson and S.M. Katz. 1995. Technical terminology: some linguistic properties and an algorithm for identification in text. Natural language engineering, 1(01):9-27.
    • (1995) Natural Language Engineering , vol.1 , Issue.1 , pp. 9-27
    • Justeson, J.S.1    Katz, S.M.2
  • 21
    • 84970769979 scopus 로고
    • The negational reference: Or the art of dissembling
    • M.H. MacRoberts and B.R. MacRoberts. 1984. The negational reference: Or the art of dissembling. Social Studies of Science, 14(1):91-94.
    • (1984) Social Studies of Science , vol.14 , Issue.1 , pp. 91-94
    • MacRoberts, M.H.1    MacRoberts, B.R.2
  • 22
    • 84858426560 scopus 로고    scopus 로고
    • Dependency tree-based sentiment classification using CRFs with hidden variables
    • Association for Computational Linguistics
    • T. Nakagawa, K. Inui, and S. Kurohashi. 2010. Dependency tree-based sentiment classification using CRFs with hidden variables. In NAACL HLT, pages 786-794. Association for Computational Linguistics.
    • (2010) NAACL HLT , pp. 786-794
    • Nakagawa, T.1    Inui, K.2    Kurohashi, S.3
  • 23
    • 84880661337 scopus 로고    scopus 로고
    • Towards multi-paper summarization using reference information
    • Citeseer
    • H. Nanba and M. Okumura. 1999. Towards multi-paper summarization using reference information. In IJCAI, volume 16, pages 926-931. Citeseer.
    • (1999) IJCAI , vol.16 , pp. 926-931
    • Nanba, H.1    Okumura, M.2
  • 24
    • 85041441290 scopus 로고    scopus 로고
    • Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews
    • Association for Computational Linguistics
    • V. Ng, S. Dasgupta, and SM Arifin. 2006. Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In Proceedings of the COLING/ACL on Main conference poster sessions, pages 611-618. Association for Computational Linguistics.
    • (2006) Proceedings of the COLING/ACL on Main Conference Poster Sessions , pp. 611-618
    • Ng, V.1    Dasgupta, S.2    Arifin, S.M.3
  • 25
    • 85141803251 scopus 로고    scopus 로고
    • Thumbs up?: Sentiment classification using machine learning techniques
    • Association for Computational Linguistics
    • B. Pang, L. Lee, and S. Vaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In EMNLP, pages 79-86. Association for Computational Linguistics.
    • (2002) EMNLP , pp. 79-86
    • Pang, B.1    Lee, L.2    Vaithyanathan, S.3
  • 30
    • 84977233801 scopus 로고
    • Science studies: Bibliometric and content analysis
    • I. Spiegel-Rösing. 1977. Science studies: Bibliometric and content analysis. Social Studies of Science, 7(1):97-113.
    • (1977) Social Studies of Science , vol.7 , Issue.1 , pp. 97-113
    • Spiegel-Rösing, I.1
  • 31
    • 84860155692 scopus 로고    scopus 로고
    • Discovering fine-grained sentiment with latent variable structured prediction models
    • O. Täckström and R. McDonald. 2011. Discovering fine-grained sentiment with latent variable structured prediction models. In Proceedings of the ECIR.
    • (2011) Proceedings of the ECIR
    • Täckström, O.1    McDonald, R.2
  • 32
    • 41449113104 scopus 로고    scopus 로고
    • Automatic classification of citation function
    • Association for Computational Linguistics
    • S. Teufel, A. Siddharthan, and D. Tidhar. 2006. Automatic classification of citation function. In EMNLP, pages 103-110. Association for Computational Linguistics.
    • (2006) EMNLP , pp. 103-110
    • Teufel, S.1    Siddharthan, A.2    Tidhar, D.3
  • 33
    • 0001100284 scopus 로고
    • Evaluation in the reporting verbs used in academic papers
    • G. Thompson and Y. Yiyun. 1991. Evaluation in the reporting verbs used in academic papers. Applied linguistics, 12(4):365.
    • (1991) Applied Linguistics , vol.12 , Issue.4 , pp. 365
    • Thompson, G.1    Yiyun, Y.2
  • 34
    • 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
  • 35
    • 33748373597 scopus 로고    scopus 로고
    • New directions in biomedical text annotation: Definitions, guidelines and corpus construction
    • W.J. Wilbur, A. Rzhetsky, and H. Shatkay. 2006. New directions in biomedical text annotation: definitions, guidelines and corpus construction. BMC bioinformatics, 7(1):356.
    • (2006) BMC Bioinformatics , vol.7 , Issue.1 , pp. 356
    • Wilbur, W.J.1    Rzhetsky, A.2    Shatkay, H.3
  • 36
    • 9444224970 scopus 로고    scopus 로고
    • Just how mad are you? Finding strong and weak opinion clauses
    • Menlo Park, CA; Cambridge, MA; London; AAAI Press;MIT Press
    • T. Wilson, J. Wiebe, and R. Hwa. 2004. Just how mad are you? Finding strong and weak opinion clauses. In Proceedings of the National Conference on Artificial Intelligence, pages 761-769. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.
    • (1999) Proceedings of the National Conference on Artificial Intelligence , pp. 761-769
    • Wilson, T.1    Wiebe, J.2    Hwa, R.3
  • 37
    • 80053247760 scopus 로고    scopus 로고
    • Recognizing contextual polarity in phrase-level sentiment analysis
    • Association for Computational Linguistics
    • T. Wilson, J. Wiebe, and P. Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In EMNLP, pages 347-354. Association for Computational Linguistics.
    • (2005) EMNLP , pp. 347-354
    • Wilson, T.1    Wiebe, J.2    Hoffmann, P.3
  • 38
    • 70349529656 scopus 로고    scopus 로고
    • Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
    • T. Wilson, J. Wiebe, and P. Hoffmann. 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
  • 39
    • 80053250358 scopus 로고    scopus 로고
    • Multilevel structured models for document-level sentiment classification
    • Cambridge, MA, October. Association for Computational Linguistics
    • A. Yessenalina, Y. Yue, and C. Cardie. 2010. Multilevel structured models for document-level sentiment classification. In Proceedings of EMNLP, pages 1046-1056, Cambridge, MA, October. Association for Computational Linguistics.
    • (2010) Proceedings of EMNLP , pp. 1046-1056
    • Yessenalina, A.1    Yue, Y.2    Cardie, C.3
  • 40
    • 85125365322 scopus 로고    scopus 로고
    • Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences
    • Association for Computational Linguistics
    • H. Yu and V. Hatzivassiloglou. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of EMNLP, pages 129-136. Association for Computational Linguistics.
    • (2003) Proceedings of EMNLP , pp. 129-136
    • Yu, H.1    Hatzivassiloglou, V.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.