Potential Emotion Word in Movie Dialog

  • Seung-Bo Park
  • Eunsoon You
  • Jason J. Jung
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 120)


Word emotion analysis is the basic step that recognizes emotions. Emotion words that express emotion on dialogs are classified into two classes such as direct and potential emotion word. Direct emotion word can represent clearly emotion and potential emotion word may represent specific emotion depending on context. Potential emotion word unlike direct emotion word is hardly extracted and identified. In this paper, we propose the method that extracts and identifies potential emotion words based on WordNet as well as direct emotion words. Potential emotion word can be extracted by measuring lexical affinity. Then, we consider the sense distance in order to minimize variation of meaning. In addition, we suggest the maximum sense distance that limits searching space and can extract the best potential emotion words.


Potential emotion word WordNet Movie dialog Lexical affinity Emotional distance 



This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MEST) (No. 2011-0017156).


  1. 1.
    Binali H, Wu C, Potdar V (2010) Computational approaches for emotion detection in text. Digital ecosystems and technologies (DEST), 4th IEEE international conference on, pp 172–177Google Scholar
  2. 2.
    Quan C, Ren F (2010) Automatic annotation of word emotion in sentences based on Ren-CECps. In: Proceedings of the seventh conference on international language resources and evaluation (LREC’10), pp 1146–1151Google Scholar
  3. 3.
    Chen L, Chen G-C, Xu C-Z, March J, Benford S (2008) Emoplayer: a media player for video clips with affective annotations. Interact Comput 20:17–28CrossRefGoogle Scholar
  4. 4.
    Salway A, Graham M (2003) Extracting information about emotions in films. In: Proceedings of the eleventh ACM international conference on multimedia (MULTIMEDIA ‘03), pp 299–302Google Scholar
  5. 5.
    Yassine M, Hajj H (2010) A framework for emotion mining from text in online social networks. IEEE international conference on data mining workshops (ICDMW), pp 1136–1142Google Scholar
  6. 6.
    Liu H, Lieberman H, Selker T (2003) A model of textual affect sensing using real-world knowledge. In: Proceedings of the 2003 international conference on intelligent user interfaces, pp 125–132Google Scholar
  7. 7.
    Ortony A, Clore GL, Collins A (1988) The cognitive structure of emotions. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  8. 8.
    Richardson R, Smeaton AF, Murphy J (1994) Using wordnet as a knowledge base for measuring semantic similarity between words. Working paper CA-1294, Dublin city university, school of computer application, Dublin, Ireland.
  9. 9.
    Willegen IV, Rothkrantz LJM, Wiggers P (2009) Lexical affinity measure between words. In: Proceedings of TSD, pp 234–241Google Scholar
  10. 10.
    Strapparava C, Valitutti A (2004) WordNet-affect: an affective extension of WordNet. In: Proceedings of the 4th international conference on language resources and evaluation, pp 1083–1086Google Scholar
  11. 11.
    Esuli A, Sebastiani F (2006) SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th conference on language resources and evaluation (LREC’06), pp 417–422Google Scholar
  12. 12.
    Elliot C (1992) The affective reasoner: a process model of emotions in a multi-agent system. PhD thesis, Northwestern University, The institute for the learning sciences, Technical report no. 32Google Scholar
  13. 13.
    Ma C, Prendinger H, Ishizuka M (2005) Emotion estimation and reasoning based on affective textual interaction. In: Proceedings of affective computing and intelligent interaction, first international conference (ACII 2005), pp 622–628Google Scholar
  14. 14.
    Park S-B, Yoo E, Kim H, Jo G-S (2010) Automatic emotion annotation of movie dialogue using WordNet. In: Proceedings of the third international conference on intelligent information and database systems, vol Part II, pp 130–139Google Scholar
  15. 15.
    Danisman T, Alpkocak A (2008) Feeler: emotion classification of text using vector space model. In: AISB 2008 convention, communication, interaction and social intelligence 2:53–59Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Graduate School of EducationInha UniversityNam-gu, IncheonSouth Korea
  2. 2.Department of French CivilizationInha UniversityNam-gu, IncheonSouth Korea
  3. 3.Department of Computer EngineeringYeungnam UniversityGyeongsanSouth Korea

Personalised recommendations