Evaluating the Impact of Syntax and Semantics on Emotion Recognition from Text

1. Chaffar, S., Inkpen, D.: Using a heterogeneous dataset for emotion analysis in text. In: Butz, C., Lingras, P. (eds.) Canadian AI 2011. LNCS, vol. 6657, pp. 62–67. Springer, Heidelberg (2011)

   Chapter  Google Scholar 

2. Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: SAC 2008: Proceedings of the ACM symposium on Applied computing, pp. 1556–1560. ACM, New York (2008)

   Google Scholar 

3. Aman, S., Szpakowicz, S.: Using Roget’s thesaurus for fine-grained emotion recognition. In: Proceedings of IJCNLP, pp. 296–302 (2008)

   Google Scholar 

4. Ekman, P.: Facial expression and emotion. American Psychologist 48, 384–392 (1993)

   Article  Google Scholar 

5. Boser, B.E., Guyon, I., Vapnik, V.: A Training Algorithm for Optimal Margin Classifiers. In: Proceedings of the 5th Annual Workshop on Computational Learning Theory (1992)

   Google Scholar 

6. Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience (1998)

   Google Scholar 

7. Collins, M., Duffy, N.: New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron. In: Proceedings of ACL 2002, Stroudsburg, PA, USA, pp. 263–270 (2002)

   Google Scholar 

8. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment Analysis of Twitter Data. In: Proceedings of the Workshop on Language in Social Media, pp. 30–38. Association for Computational Linguistics (2011)

   Google Scholar 

9. Strapparava, C., Valitutti, A.: WordNet-Affect: an affective extension of WordNet. In: Proceedings of LREC, vol. 4, pp. 1083–1086 (2004)

   Google Scholar 

10. Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of LREC, pp. 417–422 (2006)

    Google Scholar 

11. Aizerman, M., Braverman, E., Rozonoer, L.: Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25, 821–837 (1964)

    MathSciNet  Google Scholar 

12. Collins, M., Duffy, N.: Convolution kernels for natural language. In: Advances in Neural Information Processing Systems, vol. 14, pp. 625–632. MIT Press (2001)

    Google Scholar 

13. Haussler, D.: Convolution kernels on discrete structures. Technical report, Dept. of Computer Science, University of California at Santa Cruz (1999)

    Google Scholar 

14. Moschitti, A.: Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 318–329. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

15. Moschitti, A., Quarteroni, S., Basili, R., Manandhar, S.: Exploiting Syntactic and Shallow Semantic Kernels for Question/Answer Classification. In: Proceedings of ACL 2007 (2007)

    Google Scholar 

16. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C., Scholkopf, B.: Text classification using string kernels. Journal of Machine Learning Research 2, 563–569 (2002)

    Google Scholar 

17. Cancedda, N., Gaussier, E., Goutte, C., Renders, J.M.: Word sequence kernels. Journal of Machine Learning Research 3, 1059–1082 (2003)

    MathSciNet  MATH  Google Scholar 

18. Moschitti, A., Pighin, D., Basili, R.: Tree Kernels for Semantic Role Labeling. Computational Linguistics 34, 193–224 (2008)

    Article  MathSciNet  Google Scholar 

19. Chaumartin, F.R.: Upar7: a knowledge-based system for headline sentiment tagging. In: Proceedings of SemEval 2007, pp. 422–425. Association for Computational Linguistics, Stroudsburg (2007)

    Google Scholar 

20. Katz, P., Singleton, M., Wicentowski, R.: Swat-mp:the semeval-2007 systems for task 5 and task 14. In: Proceedings of the SemEval 2007, pp. 308–313. Association for Computational Linguistics, Prague (2007)

    Google Scholar 

21. Kozareva, Z., Navarro, B., Vazquez, S., Montoyo, A.: Ua-zbsa: A headline emotion classification through web information. In: Proceedings of SemEval 2007, pp. 334–337. Association for Computational Linguistics, Prague (2007)

    Google Scholar 

22. Das, D., Bandyopadhyay, S.: Identifying emotional expressions, intensities and sentence level emotion tags using a supervised framework. Emotion 1, 95–104 (2010)

    Google Scholar 

23. Aman, S., Szpakowicz, S.: Identifying expressions of emotion in text. In: Matoušek, V., Mautner, P. (eds.) TSD 2007. LNCS (LNAI), vol. 4629, pp. 196–205. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

24. Alm, E.C.O.: Affect in Text and Speech. PhD thesis, University of Illinois at Urbana-Champaign (2008)

    Google Scholar 

25. Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the International Conference on New Methods in Language Processing, Manchester, UK (1994)

    Google Scholar 

26. Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: Proceedings of ACL 2003, Stroudsburg, PA, USA, pp. 423–430 (2003)

    Google Scholar 

27. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41, 391–407 (1990)

    Article  Google Scholar 

28. Moschitti, A.: Efficient convolution kernels for dependency and constituent syntactic trees. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 318–329. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

29. Mohammad, S.M.: From once upon a time to happily ever after: Tracking emotions in mail and books. Decision Support Systems 53, 730–741 (2012)

    Article  Google Scholar 

30. Mohammad, S.: Portable features for classifying emotional text. In: Proceedings of NAACL HLT 2012, Stroudsburg, PA, USA, pp. 587–591 (2012)

    Google Scholar 

31. Bloehdorn, S., Basili, R., Cammisa, M., Moschitti, A.: Semantic Kernels for Text Classification based on Topological Measures of Feature Similarity. In: Proceedings of ICDM, Hong Kong (2006)

    Google Scholar 

Download references