Event-Level Textual Emotion Sensing Based on Common Action Distributions between Event Participants

  • Cheng-Yu Lu
  • William W. Y. Hsu
  • Jan-Ming Ho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7345)

Abstract

Automatic emotion sensing in textual data is crucial for the development of intelligent interfaces in interactive computer applications. This paper reports a high-precision, domain-independent approach for automatic emotion sensing for “events” embedded in sentences. The proposed approach is based on the common action distribution between the subject and object of an event. We have incorporated semantic labeling and web-based text mining techniques, together with a number of reference entity pairs and hand-crafted emotion generation rules to realize an event emotion detection system. Moreover, a hybrid emotion detection engine is presented by incorporating a set of predefined emotion keywords and the proposed event-level emotion detection engine. The evaluation outcome reveals a rather satisfactory result with about 73% accuracy for detecting the Happy, Sad, Fear, Angry, Surprise, Disgust, and Neutral.

Keywords

emotion sensing web mining natural language processing event 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wu, C., Chuang, Z., Lin, Y.: Emotion recognition from text using semantic labels and separable mixture models. ACM Transactions on Asian Language Information Processing (TALIP) 5(2), 165–183 (2006)CrossRefGoogle Scholar
  2. 2.
    Pradhan, S., Hacioglu, K., Ward, W., Martin, J., Jurafsky, D.: Semantic role parsing: Adding semantic structure to unstructured text. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 629–632 (2003)Google Scholar
  3. 3.
    Carreras, X., Màrquez, L.: Introduction to the CoNLL-2005 shared task: Semantic role labeling. In: Proceedings of the Ninth Conference on Computational Natural Language Learning, pp. 152–164 (2005)Google Scholar
  4. 4.
    Liu, H., Lieberman, H., Selker, T.: A model of textual affect sensing using real-world knowledge. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 125–132 (2003)Google Scholar
  5. 5.
    Ortony, A., Clore, G., Collins, A.: The cognitive structure of emotions. Cambridge Univ. Pr. (1990)Google Scholar
  6. 6.
    Alm, C., Roth, D., Sproat, R.: Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 579–586 (2005)Google Scholar
  7. 7.
    Lu, C., Lin, S., Liu, J., Cruz-Lara, S., Hong, J.: Automatic event-level textual emotion sensing using mutual action histogram between entities. Expert Systems with Applications 37(2), 1643–1653 (2010)CrossRefGoogle Scholar
  8. 8.
    Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Computational Linguistics 28(3), 245–288 (2002)CrossRefGoogle Scholar
  9. 9.
    Etzioni, O., Cafarella, M., Downey, D., Popescu, A., Shaked, T., Soderland, S., Weld, D., Yates, A.: Unsupervised named-entity extraction from the web: An experimental study. Artificial Intelligence 165(1), 91–134 (2005)CrossRefGoogle Scholar
  10. 10.
    Dang, H., Palmer, M.: The role of semantic roles in disambiguating verb senses. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 42–49 (2005)Google Scholar
  11. 11.
    Pradhan, S., Ward, W., Hacioglu, K., Martin, J., Jurafsky, D.: Shallow semantic parsing using support vector machines. In: Proceedings of HLT/NAACL 2004, p. 233 (2004)Google Scholar
  12. 12.
    Kullback, S., Leibler, R.: On information and sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Strapparava, C., Valitutti, A.: WordNet-Affect: an affective extension of WordNet. In: Proceedings of LREC, vol. 4, pp. 1083–1086 (2004)Google Scholar
  14. 14.
    Calvo, R., D’Mello, S.: Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing 1(1), 18–37 (2010)CrossRefGoogle Scholar
  15. 15.
    Ekman, P.: Facial expression and emotion. American Psychologist 48(4), 384 (1993)CrossRefGoogle Scholar
  16. 16.
    Soon, W., Ng, H., Lim, D.: A machine learning approach to coreference resolution of noun phrases. Computational linguistics 27(4), 521–544 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cheng-Yu Lu
    • 1
    • 2
  • William W. Y. Hsu
    • 1
    • 3
  • Jan-Ming Ho
    • 1
  1. 1.Institute of Information ScienceAcademia SinicaTaiwan
  2. 2.PIXNETTaiwan
  3. 3.Dep. of CSIENational Taiwan Normal UniversityTaiwan

Personalised recommendations