Abstract
Stories in digital content (e.g., movies) are usually developed using many kinds of relationships among the characters. In order to efficiently manage such contents, we want to exploit a social network (called Character-net) extracted from the stories. Since scripts are composed of several elements (i.e., scene headings, character names, dialogs, actions, etc.), we focus on analyzing interactions (e.g., dialog) among the characters to build such a social network. Most importantly, these relationships between minor and major characters can be abstracted and clustered into similar scenes. Thereby, in this paper, we propose a novel method that can cluster characters using their emotional similarity. If a minor character has a similar emotion vector tothe main character, then the minor character can be classified as a tritagonist who helps the main character. Conversely, this minor character may be clustered into another group and denoted as an antagonist. Additionally, we show the efficiency of our proposed method by experiment in this paper.
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Ajmera J, Wooters C (2003) A robust speaker clustering algorithm. In: Proc. IEEE Workshop on Automatic Speech Recognition Understanding., pp 411–416
Cour T, Jordan C, Miltsakaki E, Taskar B (2008) Movie/script: Alignment and parsing of video and text transcription, In: Proc. 10th European Conf. Computer Vision (ECCV'03)—Part 4, Lecture Notes in Computer Science, Marseille, France., pp 158–171
Danisman T, Alpkocak A (2008) Feeler: Emotion Classification of Text Using Vector Space Model, In AISB 2008 Convention. Commun InteractSoc Intell 2:53–59
Elliot C. The Affective Reasoner: A Process Model of Emotions in a Multi-agent System, PhD thesis, Northwestern University, May 1992. The Institute for the Learning Sciences, Technical Report No. 32
Esuli A, Sebastiani F (2006) SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining, In Proc. of the 5th Conf. on Language Resources and Evaluation (LREC'06), 417–422
Everingham M, Sivic J, Zisserman A (2009) Taking the bite out of automated naming of characters in TV video. Imag Vis Comput 27(5):545–559
Garg NP, Favre S, Salamin H, Hakkani TD, Vinciarelli A (2008) Role recognition for meeting participants: An approach based on lexical information and social network analysis, In Proc. ACM Multimedia Conf, 693–696
Hung H, Jayagopi D, Yeo C, Friendland G, Ba S, Ramchandran J, Mirghafori N, Gatica-Perez D (2007) Using audio and video features to classify the most dominant person in a group meeting. In: Proc. ACM Multimedia Conf., pp 835–838
Jung JJ (2012) Computational Reputation Model Based on Selecting Consensus Choices: an Empirical Study on Semantic Wiki Platform. Expert Syst Appl 39(10):9002–9007
Jung JJ (2012) Online Named Entity Recognition Method for Microtexts in Social Networking Services: a Case Study of Twitter. Expert Syst Appl 39(9):8066–8070
Jung JJ (2012) Discovering Community of Lingual Practice for Matching Multilingual Tags from Folksonomies. Comput J 55(3):337–346
Jung JJ (2012) Attribute selection-based recommendation framework for short-head user group: an empirical study by MovieLens and IMDB. Expert Syst Appl 39(4):4049–4054
Liu H, Lieberman H, Selker T (2003) A Model of Textual Affect Sensing Using Real-World Knowledge. In: Proc. of the 2003 Int. Conf. on Intelligent User Interfaces., pp 125–132
Ma C, Prendinger H, Ishizuka M (2005) Emotion Estimation and Reasoning based on Affective Textual Interaction. In: Proc. of Affective Computing and Intelligent Interaction, First Int. Conf. (ACII 2005)., pp 622–628
Neti C, Potamianos G, Luettin J, Matthews I, Glotin H, Vergyri D, Sison J, Mashari A, Zhou J (2000) Audio-visual speech recognition,Center Lang. Speech Process. Johns Hopkins Univ, Baltimore,MD
Nothelfer CE, DeLong JE, Cutting JE (2009) Shot Structure in Hollywood Film. Indiana Undergrad J Cogn Sci 4:103–113
Park S-B, Oh K-J, Jo G-S (2011) Social Network Analysis in a Movie using Character-net, Multimedia Tools and Application, Online First
Park S-B, Yoo E, Jung JJ. Automatic Potential Emotion Word in Movie Dialog, In Proc. of the Int. Conf. on IT Convergence and Security 2011 (ICTICS 2011), Lecture Notes in Electrical Engineering, Springer, 507–516, December 2011
Park S-B,Yoo E, Kim H, Jo G-S (2011) Automatic Emotion Annotation of Movie Dialogue using WordNet, In Proc. of the Third Int. Conf. Intelligent information and database systems—Volume Part II, 130–139
Quan C, Ren F (2010) Automatic Annotation of Word Emotion in Sentences based on Ren-CECps, In Proc. of the Seventh conference on International Language Resources and Evaluation (LREC'10), 1146–1151
Richardson R, Smeaton AF, Murphy J (1994) Using WordNet as a Knowledge Base for Measuring Semantic Similarity between Words, In Proc. of AICS Conf
Rienks R, Zhang D, Post W (2006) Detection and application of influence rankings in small group meetings, In Proc. Int. Conf. Multimodal Interfaces, 257–264
Ronfard R, Thuong TT (2003) A framework for aligning and indexing movies with their script. In: Proc. of the 2003 Int. Conf. Multimedia and Expo–Volume 2 (ICME 2003)., Vol. 2. IEEE Computer Society., pp 21–24
Salway A, Graham M (2003) Extracting Information about Emotions in Films, In Proc. of the eleventh ACM Int. Conf. on Multimedia (MULTIMEDIA '03), 299–302
Strapparava C, Valitutti A (2004) WordNet-Affect: an Affective Extension of WordNet, In Proc. of the 4th Int. Conf. on Language Resources and Evaluation, 1083–1086
Tsivian Y (2009) Cinemetrics, Part of the Humanities’ Cyberinfrastructure, Digital Tools in Media Studies: Analysis and Research: an Overview, Bielfeld: Transcript Verlag, pp. 93–100
Turetsky R, Dimitrova N (2004) Screenplay alignment for closed-system speaker identification and analysis of feature films. Proc IEEE Int Conf Multimed Expo 2004:1659–1662
Vinciarelli A, Fernandez F, Favre S (2006) Semantic segmentation of radio programs using social network analysis and duration distribution modeling. In: Proc. IEEE Int. Conf. Multimedia & Expo., pp 779–782
Weng CY, Chu WT, Wu JL (2009) RoleNet: movie analysis from the perspective of social network. IEEE Trans Multimed 11(2):256–271
Willegen IV, Rothkrantz LJM, Wiggers P (2009) Lexical Affinity Measure between Words, In Proc. of TSD, 234–241
Wu C, Steinbauer JR, Kuo GM. EmClustering Analysis of Diabetes Patients Basic Diagnosis Index. In AMIA 2005 Symposium Proc., 1158. AMIA, November 2005
Yassine M, Hajj H (2010) A Framework for Emotion Mining from Text in Online Social Networks, Int. Conf. onData Mining Workshops (ICDMW), 2010 IEEE, 1136–1142
Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (No. 2012R1A1A2002839).
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Jung, J.J., You, E. & Park, SB. Emotion-based character clustering for managing story-based contents: a cinemetric analysis. Multimed Tools Appl 65, 29–45 (2013). https://doi.org/10.1007/s11042-012-1133-x
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DOI: https://doi.org/10.1007/s11042-012-1133-x