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Detecting Emotion Stimuli in Emotion-Bearing Sentences

  • Diman Ghazi
  • Diana Inkpen
  • Stan Szpakowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9042)

Abstract

Emotion, a pervasive aspect of human experience, has long been of interest to social and behavioural sciences. It is now the subject of multi-disciplinary research also in computational linguistics. Emotion recognition, studied in the area of sentiment analysis, has focused on detecting the expressed emotion. A related challenging question, why the experiencer feels that emotion, has, to date, received very little attention. The task is difficult and there are no annotated English resources. FrameNet refers to the person, event or state of affairs which evokes the emotional response in the experiencer as emotion stimulus. We automatically build a dataset annotated with both the emotion and the stimulus using FrameNet’s emotions-directed frame. We address the problem as information extraction: we build a CRF learner, a sequential learning model to detect the emotion stimulus spans in emotion-bearing sentences. We show that our model significantly outperforms all the baselines.

Keywords

Emotion Recognition Noun Phrase Emotion Stimulus Sentiment Analysis Frame Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Alm, C.O., Roth, D., Sproat, R.: Emotions from Text: Machine Learning for Text-based Emotion Prediction. In: HLT/EMNLP, pp. 347–354 (2005)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Bethard, S., Martin, J.H.: Learning Semantic Links from a Corpus of Parallel Temporal and Causal Relations. In: Proc. ACL 2008 HLT Short Papers, pp. 177–180 (2008)Google Scholar
  4. 4.
    Bethard, S., Yu, H., Thornton, A., Hatzivassiloglou, V., Jurafsky, D.: Automatic Extraction of Opinion Propositions and their Holders. In: 2004 AAAI Spring Symposium on Exploring Attitude and Effect in Text, pp. 22–24 (2004)Google Scholar
  5. 5.
    Chang, D.S., Choi, K.S.: Incremental cue phrase learning and bootstrapping method for causality extraction using cue phrase and word pair probabilities. Information Processing and Management 42(3), 662–678 (2006)CrossRefGoogle Scholar
  6. 6.
    Chaumartin, F.R.: UPAR7: A knowledge-based system for headline sentiment tagging. In: Proc. 4th International Workshop on Semantic Evaluations, SemEval 2007, pp. 422–425 (2007)Google Scholar
  7. 7.
    Chen, Y., Lee, S.Y.M., Li, S., Huang, C.R.: Emotion cause detection with linguistic constructions. In: Proc. 23rd International Conference on Computational Linguistics, COLING 2010, pp. 179–187 (2010)Google Scholar
  8. 8.
    Choi, Y., Breck, E., Cardie, C.: Joint extraction of entities and relations for opinion recognition. In: Proc. 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP 2006, pp. 431–439 (2006)Google Scholar
  9. 9.
    Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Identifying sources of opinions with conditional random fields and extraction patterns. In: Proc. Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005, pp. 355–362 (2005)Google Scholar
  10. 10.
    Cohen, W.W.: Minorthird: Methods for Identifying Names and Ontological Relations in Text using Heuristics for Inducing Regularities from Data (2004), http://minorthird.sourceforge.net
  11. 11.
    Ekman, P.: An argument for basic emotions. Cognition & Emotion 6(3), 169–200 (1992)CrossRefGoogle Scholar
  12. 12.
    Feng, S., Banerjee, R., Choi, Y.: Characterizing Stylistic Elements in Syntactic Structure. In: Proc. the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, pp. 1522–1533 (2012)Google Scholar
  13. 13.
    Fillmore, C.J., Petruck, M.R., Ruppenhofer, J., Wright, A.: FrameNet in Action: The Case of Attaching. IJL 16(3), 297–332 (2003)Google Scholar
  14. 14.
    Ghazi, D., Inkpen, D., Szpakowicz, S.: Prior versus contextual emotion of a word in a sentence. In: Proc. 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, WASSA 2012, pp. 70–78 (2012)Google Scholar
  15. 15.
    Girju, R.: Automatic detection of causal relations for Question Answering. In: Proc. ACL 2003 Workshop on Multilingual Summarization and Question Answering, MultiSumQA 2003, vol. 12, pp. 76–83 (2003)Google Scholar
  16. 16.
    Girju, R., Moldovan, D.: Mining Answers for Causation Questions. In: AAAI Symposium on Mining Answers from Texts and Knowledge Bases (2002)Google Scholar
  17. 17.
    Kaplan, R.M., Berry-Rogghe, G.: Knowledge-based acquisition of causal relationships in text. Knowledge Acquisition 3(3), 317–337 (1991)CrossRefGoogle Scholar
  18. 18.
    Katz, P., Singleton, M., Wicentowski, R.: SWAT-MP: the SemEval-2007 systems for task 5 and task 14. In: Proc. 4th International Workshop on Semantic Evaluations, SemEval 2007, pp. 308–313 (2007)Google Scholar
  19. 19.
    Kim, S.M., Hovy, E.: Identifying and Analyzing Judgment Opinions. In: Proc. HLT/NAACL 2006, pp. 200–207 (2006)Google Scholar
  20. 20.
    Kozareva, Z., Navarro, B., Vázquez, S., Montoyo, A.: UA-ZBSA: A headline emotion classification through web information. In: Proc. 4th International Workshop on Semantic Evaluations, SemEval 2007, pp. 334–337 (2007)Google Scholar
  21. 21.
    Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proc. Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  22. 22.
    Lee, S.Y.M., Chen, Y., Huang, C.R.: A text-driven rule-based system for emotion cause detection. In: Proc. NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, CAAGET 2010, pp. 45–53 (2010)Google Scholar
  23. 23.
    Lee, S.Y.M., Chen, Y., Li, S., Huang, C.R.: Emotion Cause Events: Corpus Construction and Analysis. In: Proc. Seventh International Conference on Language Resources and Evaluation (LREC 2010). European Language Resources Association (ELRA), Valletta (2010)Google Scholar
  24. 24.
    Lu, C.Y., Lin, S.H., Liu, J.C., Cruz-Lara, S., Hong, J.S.: Automatic event-level textual emotion sensing using mutual action histogram between entities. Expert Systems With Applications 37(2), 1643–1653 (2010)CrossRefGoogle Scholar
  25. 25.
    Mohammad, S., Zhu, X., Martin, J.: Semantic Role Labeling of Emotions in Tweets. In: Proc. 5th, ACL Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 32–41 (2014)Google Scholar
  26. 26.
    Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proc. NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, CAAGET 2010, pp. 26–34 (2010)Google Scholar
  27. 27.
    Neviarouskaya, A., Aono, M.: Extracting Causes of Emotions from Text. In: International Joint Conference on Natural Language Processing, pp. 932–936 (2013)Google Scholar
  28. 28.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Affect Analysis Model: novel rule-based approach to affect sensing from text. Natural Language Engineering 17(1), 95–135 (2011)CrossRefGoogle Scholar
  29. 29.
    Ortony, A., Collins, A., Clore, G.L.: The cognitive structure of emotions. Cambridge University Press (1988)Google Scholar
  30. 30.
    Picard, R.W.: Affective Computing. The MIT Press (1997)Google Scholar
  31. 31.
    Pustejovsky, J., Lee, K., Bunt, H., Romary, L.: ISO-TimeML: An International Standard for Semantic Annotation. In: Proc. the Seventh International Conference n Language Resources and Evaluation (LREC 2010) (2010)Google Scholar
  32. 32.
    Scherer, K.R.: What are emotions? And how can they be measured? Social Science Information 44, 695–729 (2005)CrossRefGoogle Scholar
  33. 33.
    Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: Proc. 2008 ACM Symposium on Applied Computing, SAC 2008, pp. 1556–1560 (2008)Google Scholar
  34. 34.
    Strapparava, C., Valitutti, A.: WordNet-Affect: an Affective Extension of WordNet. In: Proc. 4th International Conference on Language Resources and Evaluation, pp. 1083–1086 (2004)Google Scholar
  35. 35.
    Tokuhisa, R., Inui, K., Matsumoto, Y.: Emotion classification using massive examples extracted from the web. In: Proc. 22nd International Conference on Computational Linguistics, COLING 2008, vol. 1, pp. 881–888 (2008)Google Scholar
  36. 36.
    Wallach, H.M.: Conditional random fields: An introduction. Tech. rep., University of Pennsylvania (2004)Google Scholar
  37. 37.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis. Computational Linguistics 35(3), 399–433 (2009)CrossRefGoogle Scholar
  38. 38.
    Wu, C.H., Chuang, Z.J., Lin, Y.C.: 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

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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