Quality & Quantity

, Volume 48, Issue 1, pp 49–62 | Cite as

A cinemetric approach to sentimental processing on story-oriented contents

  • Seung-Bo Park
  • Eunsoon You
  • Jason J. Jung


Most of stories are usually bsed on many kinds of relationships among the characters. particularly, in various digital contents, to efficiently manage, we present a novel cinemetric approach to exploit a social network (called Character-net) extracted from the stories. Since story transcripts are composed of several elements (e.g., scene headings, character names, dialogues, and actions), we focus on analyzing interactions (e.g., dialogue) among the characters to build such social networks. Most importantly, these relationships among characters can be extracted into similar scenes. Thereby, in this paper, we propose a novel method clustering characters using their sentimental similarities. If a minor character has a similar emotion vector to the main characters, then we assume that the minor character can be classified as a tritagonist who is helping the main character. Conversely, this minor character can be assumed to be clustered into another group and denoted as an antagonist. To evaluate the proposed approach, we show the efficiency of our proposed method by experiment in this paper.


Character clustering Social relationship Sentimental analysis Social network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Danisman, T., Alpkocak, A.: Feeler: emotion classification of text using vector space model. In: AISB 2008 Convention, Communication, Interaction and Social Intelligence, vol. 2, pp. 53–59 (2008)Google Scholar
  2. Elliot, C.: The affective reasoner: a process model of emotions in a multi-agent system, Ph.D. thesis, Northwestern University. The Institute for the Learning Sciences, Technical Report No. 32, (1992)Google Scholar
  3. Esuli, A., Sebastiani, F.: SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), pp. 417–422, (2006)Google Scholar
  4. Garg, N.P., Favre, S., Salamin, H., Hakkani, T. D., Vinciarelli, A.: Role recognition for meeting participants: an approach based on lexical information and social network analysis. In: Proceedings of the ACM Multimedia Conference, pp. 693–696, (2008)Google Scholar
  5. Hung, H., Jayagopi, D., Yeo, C., Friendland, G., Ba, S., Ramchandran, J., Mirghafori, N., Gatica-Perez, D.: Using audio and video features to classify the most dominant person in a group meeting. In: Proceedings of the ACM Multimedia Conference, pp. 835–838, (2007)Google Scholar
  6. Liu, H., Lieberman, H., Selker, T.: A model of textual affect sensing using real-world knowledge. In: Proceedings of the 2003 International Conference on Intelligent User Interfaces, pp. 125–132, (2003)Google Scholar
  7. Ma, C., Prendinger, H., Ishizuka, M.: Emotion estimation and reasoning based on affective textual interaction. In: Proceedings of Affective Computing and Intelligent Interaction, First International Conference (ACII 2005), pp. 622–628, (2005)Google Scholar
  8. Park, S.-B., Oh, K.-J., Jo, G.-S.: Social network analysis in a movie using Character-net, multimedia tools and application, Online First (2011a)Google Scholar
  9. Park, S.-B., Yoo, E., Kim, H., Jo, G.-S.: Automatic emotion annotation of movie dialogue using WordNet. In: Proceedings of the Third Intenational Conference on Intelligent Information and Database Systems—vol Part II, pp. 130–139, (2011b)Google Scholar
  10. Park, S.-B., Yoo, E., Jung, J.J.: Automatic potential emotion word in movie dialog. In: Proceedings of the International Conference on IT Convergence and Security 2011 (ICTICS 2011), Lecture Notes in Electrical Engineering, pp. 507–516. Springer, Dec (2011c)Google Scholar
  11. Quan, C., Ren, F.: 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. 1146–1151, (2010)Google Scholar
  12. Richardson, R., Smeaton, A. F., Murphy, J.: Using WordNet as a knowledge base for measuring semantic similarity between words. In: Proceedings of AICS Conference, (1994)Google Scholar
  13. Rienks, R., Zhang, D., Post, W.: Detection and application of influence rankings in small group meetings. In: Proceedings of the International Conference Multimodal Interfaces, pp. 257–264, (2006)Google Scholar
  14. Salway, A., Graham, M.: Extracting information about emotions in films. In: Proceedings of the Eleventh ACM International Conference on Multimedia (MULTIMEDIA ’03), pp. 299–302, (2003)Google Scholar
  15. Strapparava, C., Valitutti, A.: WordNet-affect: an affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation, pp. 1083–1086, (2004)Google Scholar
  16. Vinciarelli, A., Fernandez, F., Favre, S.: Semantic segmentation of radio programs using social network analysis and duration distribution modeling. In: Proceedings of the IEEE International Conference Multimedia & Expo., pp. 779–782, (2006)Google Scholar
  17. Weng C.Y., Chu W.T., Wu J.L.: RoleNet: movie analysis from the perspective of social network. IEEE Trans. Multimed. 11(2), 256–271 (2009)CrossRefGoogle Scholar
  18. Willegen, I.V., Rothkrantz, L.J.M., Wiggers, P.: Lexical affinity measure between words. In: Proceedings of TSD, pp. 234–241, (2009)Google Scholar
  19. Wu, C., Steinbauer, J.R., Kuo, G.M.: Em clustering analysis of diabetes patients basic diagnosis index. In: AMIA 2005 Symposium Proceedings, 1158. AMIA, Nov (2005)Google Scholar
  20. Yassine, M., Hajj, H.: A framework for emotion mining from text in online social networks. International Conference on Data Mining Workshops (ICDMW), 2010 IEEE, pp. 1136–1142, (2010)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Inha UniversityIncheonSouth Korea
  2. 2.Dankook UniversityGyeonggi-doSouth Korea
  3. 3.Department of Computer EngineeringYeungnam UniversityGyeongsanSouth Korea

Personalised recommendations