Methods for creating semantic orientation dictionaries

Proceedings of the 5th International Conference on Language Resources and Evaluation, LREC 2006

Volumn , Issue , 2006, Pages 427-432

  Taboada, Maite ^a   Anthony, Caroline ^a   Voll, Kimberly ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

SEMANTICS;

MUTUAL INFORMATIONS; POINTWISE MUTUAL INFORMATION; POTENTIAL PROBLEMS; SEARCH ENGINE-ALTAVISTA; SEED WORDS; SEMANTIC ORIENTATION; TARGET WORDS;

SEARCH ENGINES;

EID: 84907007413     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Bai, X., Padman, R. & Airoldi, E. (2004). Sentiment Ext raction from Unstructured Text Using Tabu Search - Enhanced Markov Blanket (Technical Report CMU-ISRI-04-127). Pittsburgh: Carnegie Mellon University.
2. Esuli, A. & Sebastiani, F. (2005). Determining the semantic orientation of terms through gloss classification. Proceedings of the 14th ACM International Conference on Information and Knowledge Management. Bremen, Germany.
3. Gamon, M. (2004). Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis. Proceedings of COLING 2004 (pp. 841-847). Geneva, Switzerland.
4. Hatzivassiloglou, V. & McKeown, K. (1997). Predicting the semantic orientation of adjectives. Proceedings of 35th Meeting of the Association for Computational Linguistics (pp. 174-181). Mad rid, Spain.
5. Kamps, J., Marx, M., Mokken, R.J. & de Rijke, M. (2004). Using WordNet to measure semantic orientation of adjectives. Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004) (pp. 1115-1118). Lisbon, Portugal.
6. Kennedy, A. & Inkpen, D. (2006). Sentiment classification of movie and product reviews using contextual valence shifters. Computational Intelligence, to appear.
7. Landauer, T.K. & Dumais, S.T. (1997). A solution to Plato's problem: The latent semant ic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211-240.
8. Mann, W.C. & Thompson, S.A. (1988). Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3), 243-281.
9. Mulder, M., Nijholt, A., den Uyl, M. & Terpstra, P. (2004). A lexical grammatical implementation of affect. In P. Sojka, I. Kopecek & K. Pala (Eds.), Proceedings of the 7th International Conference on Text, Speech & Dialogue (TSD 2004) (pp. 171-178). Berlin: Springer.
10. Nigam, K. & Hurst, M. (2004). Towards a robust metric of opinion. Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text (pp. 98-105). Stanford University, California.
11. rd ACL. Ann Arbor, MI.
12. Pang, B., Lee, L. & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using Machine Learn ing techniques. Proceedings of Conference on Empirical Methods in NLP (pp. 79-86).
13. Rapp, R. (2004). A freely available automatically generated thesaurus of related words. Proceedings of 4th International Conference on Language Resources and Evaluation (LR EC 2004). Lisbon, Portugal.
14. Stone, P.J. (1997). Thematic text analysis: New agendas for analyzing text content. In C. Roberts (Ed.), Text Analysis for the Social Sciences. Mahwah, NJ: Lawrence Erlbaum.
15. Stone, P.J., Dunphy, D.C., Smith, M.S. & Ogilvie, D.M. (1966). The General Inquirer: A Computer Approach to Content Analysis. Cambridge, MA: MIT Press.
16. Taboada, M. & Grieve, J. (2004). Analyzing appraisal automatically. Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text (pp. 158-161). Stanford University, CA.
17. Turney, P. (2001). Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. Proceedings of the 12th European Conference on Machine Learning (ECML-2001). Freiburg, Germany.
18. Turney, P. (2002). Thumbs up or thumbs down? Semant ic orientation applied to unsupervised classification of reviews. Proceedings of 40th ACL (pp. 417-424).
19. Turney, P. & Littman, M. (2002). Unsupervised learning of semantic orientation from a hundred-billion-word corpus (No. ERB-1094, NRC #44929): National Research Council of Canada.
20. Turney, P. & Littman, M. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21(4), 315-346.
21. Wiebe, J. & Riloff, E. (2005). Creating subjective and objective sentence classifiers from unannotated texts. Proceedings of Sixth International Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2005). Mexico City, Mexico.
22. Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 1(2).
23. Yang, Y. (1999). An evaluation of statistical approaches to text classification. Journal of Information Retrieval, 1(1-2), 67-88.
24. Zaenen, A. & Polanyi, L. (2004). Contextual valence shifters. Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text (pp. 106-111). Stanford University, CA.