Sentence-level sentiment polarity classification using a linguistic approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7008 LNCS, Issue , 2011, Pages 77-87

  Tan, Luke Kien Weng ^a   Na, Jin Cheon ^a   Theng, Yin Leng ^a   Chang, Kuiyu ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

linguistic approach; polarity classification; Sentiment analysis

Indexed keywords

BASELINE MODELS; COMPLEX RELATIONSHIPS; DEPENDENCY PARSING; FINE-GRAINED ANALYSIS; FUNCTIONAL RELATION; LINGUISTIC APPROACH; PHRASE ANALYSIS; POLARITY CLASSIFICATION; PREDICTION MODEL; REFINED ANALYSIS; SENTIMENT ANALYSIS; TEXTUAL DATA;

DATA MINING; DIGITAL LIBRARIES; FORMAL LANGUAGES; LINGUISTICS; MATHEMATICAL MODELS; SEMANTICS;

CLASSIFICATION (OF INFORMATION);

EID: 80455140260     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24826-9_13     Document Type: Conference Paper

References (14)

1. Cohen, J.A.: Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37-46 (1960)
2. Hassan, A., Qazvinian, V., Radev, D.R.: What's with the attitude? A study of participant attitude in multi-party online discussions. In: Empirical Methods on Natural Language Processing, pp. 1245-1255. ACL (2010)
3. Jakob, N., Weber, S.H., Muller, M.C., Gurevych, I.: Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. In: Conference on Information and Knowledge Management, pp. 57-64. ACM, New York (2009)
4. Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22(2), 110-125 (2006)
5. Moilanen, K., Pulman, S.: Sentiment Composition. In: Recent Advances in Natural Language Processing, pp. 378-382 (2007)
6. Polanyi, L., Zaenen, A.: Contextual Valence Shifters. Computing Attitude and Affect in Text: Theory and Applications 20, 1-10 (2006)
7. Quirk, R., Greenbaum, S., Leech, G., Svartvik, J.: A Comprehensive Grammar of the English Language. Longman, Redwood City (1985)
8. Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Empirical Methods on Natural Language Processing, pp. 105-112. ACL (2003)
9. Shaikh, M.A.M., Prendinger, H., Ishizuka, M.: Sentiment Assessment of Text By Analyzing Linguistic Features And Contextual Valence Assignment. Applied Artificial Intelligence 22(6), 558-601 (2008)
10. Thet, T.T., Na, J.-C., Khoo, C.S.G.: Aspect-Based Sentiment Analysis of Movie Reviews on Discussion Boards. Journal of Information Science 36(6), 823-848 (2010)
11. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistic 35(3), 399-433 (2009)
12. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347-354. ACL (2005)
13. Wilson, T., Wiebe, J., Hwa, R.: Recognizing Strong and Weak Opinion Clauses. Computational Intelligence 22(2), 73-99 (2006)
14. Zafarani, R., Liu, H.: Social Computing Data Repository at ASU Arizona State University, School of Computing, Informatics and Decision Systems Engineering (2009)