Vincent Labatut
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A character network is a graph extracted from a narrative in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fiction (e.g., novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties that make these tasks harder.
This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches and the most promising perspectives.
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- A. Agarwal, S. Balasubramanian, J. Zheng, and S. Dash. 2014. Parsing screenplays for extracting social networks from movies. In Proceedings of the 3rd Workshop on Computational Linguistics for Literature (CLFL'14). ACL, 50--58. http://www.aclweb.org/anthology/W14-0907.Google Scholar
- A. Agarwal, A. Corvalan, and J. R. O. Jensen. 2012. Social network analysis of Alice in Wonderland. In Proceedings of the Workshop on Computational Linguistics for Literature (NAACL'12). http://www.cs.columbia.edu/apoorv/Homepage/Publications_files/naacl2012.pdf.Google Scholar
- A. Agarwal, A. Kotalwar, and O. Rambow. 2013. Automatic extraction of social networks from literary text: A case study on Alice in Wonderland. In Proceedings of the International Joint Conference on Natural Language Processing (IFCNLP'13). 1202--1208. http://www.aclweb.org/website/old_anthology/I/I13/I13-1171.pdf.Google Scholar
- C. Aggarwal and K. Subbian. 2014. Evolutionary network analysis: A survey. ACM Comput. Surv. 47, 1 (2014), 10. Google ScholarDigital Library
- R. Alberich, J. Miro-Julia, and F. Rossello. 2002. Marvel Universe looks almost like a real social network. Retrieved from: arXiv cond-mat.dis-nn (2002), cond--mat/0202174. http://arxiv.org/abs/cond-mat/0202174Google Scholar
- D. R. Amancio. 2015. Network analysis of named entity interactions in written texts. EPL 5, 114 (2015), 58005.Google Scholar
- M. C. Ardanuy and C. Sporleder. 2014. Structure-based clustering of novels. In Proceedings of the 3rd Workshop on Computational Linguistics for Literature. 31--39. http://www.aclweb.org/anthology/W14-0905.Google Scholar
- M. C. Ardanuy and C. Sporleder. 2015. Clustering of novels represented as social networks. Linguistic Issues in Language Technology 12 (2015). http://csli-lilt.stanford.edu/ojs/index.php/LiLT/article/view/60.Google Scholar
- O. Augereau, M. Iwata, and K. Kise. 2018. A survey of comics research in computer science. J. Imaging 4, 7 (2018), 87.Google ScholarCross Ref
- A.-L. Barabási and R. Albert. 1999. Emergence of scaling in random networks. Science 286, 5439 (1999), 509--512.Google Scholar
- A. Bolioli, M. Casu, M. Lana, and R. Roda. 2013. Exploring the betrothed lovers. In Proceedings of the CMN. 30--35.Google Scholar
- D. Bordwell and K. Thompson. 1993. Film Art: An Introduction. McGraw-Hill. https://www.mhprofessional.com/9780073535104-usa-film-art-an-introduction.Google Scholar
- C. Bornet and F. Kaplan. 2017. A simple set of rules for characters and place recognition in French novels. Front. Digital Hum. 4 (2017), 6.Google Scholar
- G. Bossaert and N. Meidert. 2013. “We are only as strong as we are united, as weak as we are divided” A dynamic analysis of the peer support networks in the Harry Potter books. Open J. Appl. Sci. 3 (2013), 174--185.Google ScholarCross Ref
- X. Bost, S. Gueye, V. Labatut, M. Larson, G. Linarès, D. Malinas, and R. Roth. 2019. Remembering winter was coming: Character-oriented video summaries of TV series. Multimed. Tools Appl. (in revision) (2019).Google Scholar
- X. Bost, V. Labatut, S. Gueye, and G. Linarès. 2016. Narrative smoothing: Dynamic conversational network for the analysis of TV Series plots. In Proceedings of the Advances in Social Networks Analysis and Mining - 2nd International Workshop on Dynamics in Networks. 1111--1118. Google ScholarDigital Library
- X. Bost and G. Linarès. 2014. Constrained speaker diarization of TV series based on visual patterns. In Proceedings of the IEEE Spoken Language Technology Workshop. 390--395.Google Scholar
- X. Bost, G. Linarès, and S. Gueye. 2015. Audiovisual speaker diarization of TV series. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 4799--4803.Google Scholar
- H. Bredin and G. Gelly. 2016. Improving speaker diarization of TV series using talking-face detection and clustering. In Proceedings of the ACM International Conference on Multimedia. 157--161. Google ScholarDigital Library
- A. Celikyilmaz, D. Hakkani-Tur, H. He, G. Kondrak, and D. Barbosa. 2010. The actor-topic model for extracting social networks in literary narrative. In Proceedings of the NIPS Workshop: Machine Learning for Social Computing. https://webdocs.cs.ualberta.ca/denilson/files/publications/nips2010.pdf.Google Scholar
- S. Chaturvedi, S. Srivastava, H. Daumé, and C. Dyer. 2016. Modeling evolving relationships between characters in literary novels. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2704--2710. https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12408. Google ScholarDigital Library
- H. Y. Chen and J. D. Choi. 2016. Character identification on multiparty conversation. In Proceedings of the 17th Annual SIGdial Meeting on Discourse and Dialogue. 90--100. http://www.aclweb.org/anthology/W16-3612.Google Scholar
- W.-T. Chu and W.-W. Li. 2017. Manga FaceNet: Face detection in manga based on deep neural network. In Proceedings of the ACM International Conference on Multimedia Retrieval. 412--415. Google ScholarDigital Library
- P. Cipresso and G. Riva. 2016. Computational psychometrics meets Hollywood. Front. Psychol. 7 (2016), 1753.Google ScholarCross Ref
- A. Clauset and N. Eagle. 2007. Persistence and periodicity in a dynamic proximity network. In Proceedings of the Workshop on Computational Methods for Dynamic Interaction Networks (DIMACS'07). http://arxiv.org/pdf/1211.7343Google Scholar
- P. Clément, T. Bazillon, and C. Fredouille. 2011. Speaker diarization of heterogeneous web video files. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 4432--4435.Google Scholar
- L. Ding and A. Yilmaz. 2010. Learning relations among movie characters: A social networks perspective. In Proceedings of the European Conference on Computer Vision. Lecture Notes in Computer Science, Vol. 6314. 410--423. Google ScholarDigital Library
- L. Ding and A. Yilmaz. 2011. Inferring social relations from visual concepts. In Proceedings of the International Conference on Computer Vision. 699--706. Google ScholarDigital Library
- M. Edwards, L. Mitchell, J. Tuke, and M. Roughan. 2018. The one comparing narrative social network extraction techniques. arXiv cs.SI (2018), 1811.01467. https://arxiv.org/abs/1811.01467Google Scholar
- M. Elsner. 2012. Character-based kernels for novelistic plot structure. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. 634--644. http://www.aclweb.org/anthology/E12-1065. Google ScholarDigital Library
- D. K. Elson. 2012. Modeling Narrative Discourse. Ph.D. Dissertation. Columbia University. http://www.cs.columbia.edu/delson/pubs/Modeling-Narrative-Discourse_Elson_R4.pdf. Google ScholarDigital Library
- D. K. Elson, N. Dames, and K. R. McKeown. 2010. Extracting social networks from literary fiction. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. 138--147. http://dl.acm.org/citation.cfm?id=1858696 Google ScholarDigital Library
- D. K. Elson and K. R. McKeown. 2010. Automatic attribution of quoted speech in literary narrative. In Proceedings of the 24th AAAI Conference on Artificial Intelligence. 1013--1019. https://dl.acm.org/citation.cfm?id=2898607.2898769 Google ScholarDigital Library
- P. Ercolessi. 2013. Extraction multimodale de la structure narrative des épisodes de séries télévisées. PhD Thesis. Université de Toulouse. http://www.theses.fr/2013TOU30131.Google Scholar
- M. Falk. 2016. Making connections: Network analysis, the bildungsroman and the world of The Absentee. J. Lang. Lit. Cult. 63, 2--3 (2016), 107--122.Google ScholarCross Ref
- S. Ghannay, A. Caubriere, Y. Estève, N. Camelin, E. Simonnet, A. Laurent, and E. Morin. 2018. End-to-end named entity and semantic concept extraction from speech. In Proceedings of the IEEE Spoken Language Technology Workshop.Google Scholar
- K. Glass and S. Bangay. 2006. Hierarchical rule generalisation for speaker identification in fiction books. In Proceedings of the Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries. 31--40. Google ScholarDigital Library
- P. M. Gleiser. 2007. How to become a superhero. J. Stat. Mech. 2007, 9 (2007), P09020.Google ScholarCross Ref
- P. J. Gorinski and M. Lapata. 2015. Movie script summarization as graph-based scene extraction. In Proceedings of the Annual Conference of the North American Chapter of the ACL. 1066--1076. http://www.aclweb.org/anthology/N15-1113.Google Scholar
- M. Grandjean. 2017. Comparing the relational structure of the Gospels. DBL 2 (2017). In press. http://www.martingrandjean.ch/communications.Google Scholar
- S. Grayson, K. Wade, G. Meaney, and D. Greene. 2016. The sense and sensibility of different sliding windows in constructing co-occurrence networks from literature. In Proceedings of the International Workshop on Computational History and Data-Driven Humanities. 65--77.Google Scholar
- A. Grener, M. Luczak-Roesch, E. Fenton, and T. Goldfinch. 2017. Towards a Computational Literary Science: A Computational Approach to Dickens’ Dynamic Character Networks. Technical Report. Zenodo.Google Scholar
- H. He, D. Barbosa, and G. Kondrak. 2013. Identification of speakers in novels. In Proceedings of the ACL. 1312--1320.Google Scholar
- F. Heider. 1946. Attitudes and cognitive organization. J. Psychol. 21, 1 (1946), 107--112.Google ScholarCross Ref
- L. Hettinger, M. Becker, I. Reger, F. Jannidis, and A. Hotho. 2015. Genre classification on German novels. In Proceedings of the 26th International Workshop on Database and Expert Systems Applications. Google ScholarDigital Library
- A. J. Holanda, M. Matias, S. M. S. P. Ferreira, G. M. L. Benevides, and O. Kinouchi. 2017. Character networks and book genre classification. arXiv cs.SI (2017), 1704.08197. https://arxiv.org/abs/1704.08197Google Scholar
- S. Hutchinson, V. Datla, and M. M. Louwerse. 2012. Social networks are encoded in language. In Proceedings of the 34th Annual Conference of the Cognitive Science Society. 491--496. https://mindmodeling.org/cogsci2012/papers/0096/paper0096.pdf.Google Scholar
- M. Iyyer, A. Guha, S. Chaturvedi, J. Boyd-Graber, and H. Daumé. 2016. Feuding families and former friends: Unsupervised learning for dynamic fictional relationships. In Proceedings of the Conference of the North American Chapter of the ACL: Human Language Technologies. 1534--1544.Google Scholar
- F. Jannidis, I. Reger, M. Krug, L. Weimer, L. Macharowsky, and F. Puppe. 2016. Comparison of methods for the identification of main characters in German novels. In Proceedings of the DH. 578--582. http://dh2016.adho.org/abstracts/297.Google Scholar
- P. A. Jayannavar, A. Agarwal, M. Ju, and O. Rambo. 2015. Validating literary theories using automatic social network extraction. In Proceedings of the 4th Workshop on Computational Linguistics for Literature. 32--41. http://anthology.aclweb.org/W/W15/W15-07.pdf#page=46.Google Scholar
- J. Jovanovic, E. Bagheri, J. Cuzzola, D. Gasevic, Z. Jeremic, and R. Bashash. 2014. Automated semantic tagging of textual content. IT Professional 16, 6 (2014), 38--46.Google ScholarCross Ref
- J. J. Jung, E. You, and S.-B. Park. 2013. Emotion-based character clustering for managing story-based contents: A cinemetric analysis. Multimed. Tools App. 65, 1 (2013), 29--45. Google ScholarDigital Library
- F. Karsdorp, P. van Kranenburg, T. Meder, and A. van den Bosch. 2012. Casting a spell: Identification and ranking of actors in folktales. In Proceedings of the 2nd Workshop on Annotation of Corpora for Research in the Humanities. https://pure.knaw.nl/ws/files/481270/karsdorp_et_al2012b.pdf.Google Scholar
- A. Kazantseva and S. Szpakowicz. 2010. Summarizing short stories. Comput. Linguist. 36, 1 (2010), 71--109. Google ScholarDigital Library
- D. E. Knuth. 1993. The Stanford GraphBase: A Platform for Combinatorial Computing. Vol. 37. Addison-Wesley Reading. http://www-cs-faculty.stanford.edu/knuth/sgb.html. Google Scholar
- V. Krishnan and J. Eisenstein. 2015. “You’re Mr. Lebowski, I’m the Dude”: Inducing address term formality in signed social networks. In Proceedings of the Annual Conference of the North American Chapter of the ACL. 1616--1626. http://www.aclweb.org/anthology/N15-1185.Google Scholar
- M. Krug, F. Puppe, F. Jannidis, L. Macharowsky, I. Reger, and L. Weimer. 2015. Rule-based coreference resolution in German historic novels. In Proceedings of the 4th Workshop on Computational Linguistics for Literature. 98--104. http://www.aclweb.org/anthology/W15-0711.Google Scholar
- M. Krug, I. Reger, F. Jannidis, L. Weimer, N. Madarász, and F. Puppe. 2017. Overcoming data sparsity for relation detection in German novels. In Proceedings of the Digital Humanities. https://dh2017.adho.org/abstracts/340/340.pdf.Google Scholar
- D. Kruger, M. Fisher, and I. Jobling. 2003. Proper and dark heroes as DADS and CADS. Human Nature 14, 3 (2003), 305--317.Google ScholarCross Ref
- H.-C. Kwon and K.-H. Shim. 2017. An improved method of character network analysis for literary criticism: A case study of Hamlet. Int. J. Contents 13, 3 (2017), 43--48.Google Scholar
- J. Lee and T.-S. Wong. 2016. Conversational network in the Chinese Buddhist Canon. Open. Ling. 2, 1 (2016), 427--436.Google Scholar
- J. Lee and C. Y. Yeung. 2012. Extracting networks of people and places from literary texts. In Proceedings of the 26th Pacific Asia Conference on Language, Information and Computation. 209--218. https://aclanthology.info/papers/Y12-1022/y12-1022.Google Scholar
- O.-J. Lee and J. J. Jung. 2019. Modeling affective character network for story analytics. Future Generation Computer Systems 92 (2019), 458--478.Google ScholarCross Ref
- J. Li, D. Yang, and P. Lv. 2019. Visualize classic play’s composing patterns. Multimed. Tools App. 78, 5 (2019), 5989--6012. Google ScholarDigital Library
- C. Liang, Y Zhang, J. Cheng, and C.-S. Xu. 2009. A novel role-based movie scene segmentation method. In Proceedings of the Pacific-Rim Conference on Multimedia. Lecture Notes in Computer Science, Vol. 5879. 917--922. Google ScholarDigital Library
- D. Liu and L. Albergante. 2017. Balance of thrones: A study on Game of Thrones Game of Thrones. arXiv cs.SI (2017), 1707.05213. https://arxiv.org/abs/1707.05213Google Scholar
- P. Mac Carron and R. Kenna. 2012. Universal properties of mythological networks. EPL 99, 2 (2012), 28002.Google ScholarCross Ref
- C. Makris and P. Vikatos. 2016. Community detection of screenplay characters. In Proceedings of the 12th IFIP International Conference on Artificial Intelligence Applications and Innovations. IFIP Advances in Information and Communication Technology, Vol. 475. 463--470.Google ScholarCross Ref
- N. Mamede and P. Chaleira. 2004. Character identification in children stories. In Proceedings of the International Conference on Natural Language Processing in Spain. Lecture Notes in Computer Science, Vol. 3230. 82--90.Google ScholarCross Ref
- V. H. Masías, P. Baldwin, S. Laengle, A. Vargas, and F. A. Crespo. 2017. Exploring the prominence of Romeo and Juliet’s characters using weighted centrality measures. Digit. Scholarsh. Hum. 32, 4 (2017), 837--858.Google Scholar
- D. McNary. 2019. 2018. Worldwide Box Office Hits Record as Disney Dominates. Retrieved on February 20, 2019 from https://variety.com/2019/film/news/box-office-record-disney-dominates-1203098075/.Google Scholar
- S. Min and J. Park. 2016. Mapping out narrative structures and dynamics using networks and textual information. arXiv cs.CL (2016), 1604.03029. http://arxiv.org/abs/1604.03029Google Scholar
- S. Min and J. Park. 2016. Network science and narratives: Application to Les Misérables. In Proceedings of the 7th Workshop on Complex Networks CompleNet. Studies in Computational Intelligence, Vol. 644. 257--265.Google Scholar
- F. Moretti. 2011. Network theory, plot analysis. Stanford Lit. Lab. 2 (2011). http://litlab.stanford.edu/LiteraryLabPamphlet2.pdf.Google Scholar
- P. Mutton. 2004. Inferring and visualizing social networks on internet relay chat. In Proceedings of the 8th International Conference on Information Visualisation. 35--43. Google ScholarDigital Library
- E. T. Nalisnick and H. S. Baird. 2013. Character-to-character sentiment analysis in Shakespeare’s plays. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. http://www.aclweb.org/anthology/P13-2085.Google Scholar
- C.-J. Nan, K.-M. Kim, and B.-T. Zhang. 2015. Social network analysis of TV drama characters via deep concept hierarchies. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 831--836. Google ScholarDigital Library
- D. Oelke, D. Kokkinakis, and D. A. Keim. 2013. Fingerprint matrices: Uncovering the dynamics of social networks in prose literature. Comput. Graph. Forum 32, 3pt4 (Jun 2013), 371--380. Google ScholarDigital Library
- D. Oelke, D. Kokkinakis, and M. Malm. 2012. Advanced visual analytics methods for literature analysis. In Proceedings of the 6th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. 35--44. https://dl.acm.org/citation.cfm?id=2390364 Google ScholarDigital Library
- T. O’Keefe, S. Pareti, J. R. Curran, I. Koprinska, and M. Honnibal. 2012. A sequence labelling approach to quote attribution. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 790--799. https://dl.acm.org/citation.cfm?id=2391033 Google ScholarDigital Library
- G.-M. Park, S.-H. Kim, H.-R. Hwang, and H.-G. Cho. 2013. Complex system analysis of social networks extracted from literary fictions. Int. J. Mach. Comput. 3, 1 (2013), 107--111.Google Scholar
- S.-B. Park, K.-J. Oh, and G.-S. Jo. 2012. Social network analysis in a movie using Character-Net. Multimed. Tools App. 59, 2 (Jan. 2012), 601--627. Google ScholarDigital Library
- F. Pope, R. A. Shirvani, Mugizi R. Rwebangira, M. Chouikha, A. Taylor, A. Alarcon Ramirez, and A. Torfi. 2015. Automatic detection of small groups of persons, influential members, relations and hierarchy in written conversations using fuzzy logic. In Proceedings of the International Conference on Data Mining. 155--161. https://arxiv.org/abs/1610.01720v1Google Scholar
- S. D. Prado, S. R. Dahmen, A. L. C. Bazzan, P. Mac Carron, and R. Kenna. 2016. Temporal network analysis of literary texts. Adv. Complex Syst. 19, 3 (2016), 1650005.Google ScholarCross Ref
- B. Rieck and H. Leitte. 2016. “Shall I compare thee to a network?”: Visualizing the topological structure of Shakespeare’s plays. In Proceedings of the 1st Workshop on Visualization for the Digital Humanities.Google Scholar
- C. Rigaud, T. N. Le, J.-C. Burie, J.-M. Ogier, M. Iwata, E. Imazu, and K. Kise. 2015. Speech balloon and speaker association for comics and manga understanding. In Proceedings of the 13th International Conference on Document Analysis and Recognition. 351--355. Google ScholarDigital Library
- D. Robson. 2016. Heroes and villains. The Psychol. 29 (2016), 610--613. https://thepsychologist.bps.org.uk/heroes-and-villains.Google Scholar
- D. Robson. 2018. Our fiction addiction: Why humans need stories. Retrieved on June 28, 2018 from http://www.bbc.com/culture/story/20180503-our-fiction-addiction-why-humans-need-stories.Google Scholar
- Y. Rochat and F. Kaplan. 2014. Analyse de réseaux sur les personnages des Confessions de Jean-Jacques Rousseau. Cahiers Num. 10, 3 (2014), 109--133.Google Scholar
- Y. Rochat and M. Triclot. 2017. Les réseaux de personnages de science-fiction. ReS Futurae 10 (2017).Google Scholar
- G. A. Sack. 2012. Character networks for narrative generation. In Proceedings of the 8th Artificial Intelligence and Interactive Digital Entertainment Conference - Intelligent Narrative Technologies Workshop. 38--43. http://www.aaai.org/ocs/index.php/AIIDE/AIIDE12/paper/view/5550.Google Scholar
- J. Seo, G.-M. Park, S.-H. Kim, and H.-G. Cho. 2013. Characteristic analysis of social network constructed from literary fiction. In Proceedings of the International Conference on Cyberworlds. 147--150. Google ScholarDigital Library
- J.-K. Seo, S.-H. Kim, H.-S. Tak, and H.-G. Cho. 2014. A structural analysis of literary fictions with social network framework. In Proceedings of the 29th Annual ACM Symposium on Applied Computing. 634--640. Google ScholarDigital Library
- D. Smith, P. Schlaepfer, K. Major, M. Dyble, A. E. Page, J. Thompson, N. Chaudhary, G. D. Salali, R. Mace, L. Astete, M. Ngales, L. Vinicius, and A. Bamberg Migliano. 2017. Cooperation and the evolution of hunter-gatherer storytelling. Nature Comm. 8 (2017), 1853.Google ScholarCross Ref
- K. Somandepalli, N. Kumar, T. Guha, and S. S. Narayanan. 2018. Unsupervised discovery of character dictionaries in animation movies. IEEE T. Multimedia. 20, 3 (2018), 539--551. Google ScholarDigital Library
- S. Srivastava, S. Chaturvedi, and T. Mitchell. 2016. Inferring interpersonal relations in narrative summaries. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2807--2813. https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12173. Google ScholarDigital Library
- J. Stiller and M. Hudson. 2005. Weak links and scene cliques within the small world of Shakespeare. J. Cult. Evol. Psychol. 3, 1 (2005), 57--73.Google ScholarCross Ref
- J. Stiller, D. Nettle, and R. I. M. Dunbar. 2003. The small world of Shakespeare’s plays. Hum. Nature 14, 4 (2003), 397--408.Google ScholarCross Ref
- M. Stommel, L. I. Merhej, and M. G. Müller. 2012. Segmentation-free detection of comic panels. In Proceedings of the International Conference on Computer Vision and Graphics. Lecture Notes in Computer Science, Vol. 7594. 633--640.Google ScholarCross Ref
- M. Stricker, O. Augereau, K. Kise, and M. Iwata. 2018. Facial landmark detection for manga images. arXiv cs.CV (2018), 1811.03214. https://arxiv.org/abs/1811.03214Google Scholar
- S. Sudhahar and N. Cristianini. 2013. Automated analysis of narrative content for digital humanities. Int. J. Adv. Comp. Sci. 3, 9 (2013), 440--447.Google Scholar
- C. Suen, L. Kuenzel, and S. Gil. 2013. Extraction and analysis of character interaction networks from plays and movies. In Proceedings of the Digital Humanities. http://snap.stanford.edu/class/cs224w-2011/proj/laneyk_Finalwriteup_v1.pdf.Google Scholar
- K. Takayama, H. Johan, and T. Nishita. 2012. Face detection and face recognition of cartoon characters using feature extraction. In Proceedings of the IIEEJ Image Electronics and Visual Computing Workshop. http://www.iieej.org/trans/IEVC/IEVC2012/PDF/4B-1.pdf.Google Scholar
- M. S. A. Tan, E. A. Ujum, and K. Ratnavelu. 2014. A character network study of two Sci-Fi TV series. In Proceedings of the Frontiers in Physics (AIP Conference Proceedings), Vol. 1588--246.Google Scholar
- M. Tandiwe Myambo. 2016. How reading fiction can help students understand the real world. Retrieved on February 02, 2019 from https://theconversation.com/how-reading-fiction-can-help-students-understand-the-real-world-52908.Google Scholar
- Q. D. Tran, D. Hwang, and J. J. Jung. 2015. Movie summarization using characters network analysis. In Proceedings of the 7th International Conference on Computational Collective Intelligence. Lecture Notes in Computer Science, Vol. 9329. Springer, 390--399.Google Scholar
- Q. D. Tran, D. Hwang, and J. J. Jung. 2017. Character-based indexing and browsing with movie ontology. J. Intell. Fuzzy Syst. 32, 2 (2017), 1229--1240.Google ScholarCross Ref
- M. Trovati and J. Brady. 2014. Towards an automated approach to extract and compare fictional networks. In Proceedings of the 25th International Workshop on Database and Expert Systems Applications.Google Scholar
- C.-M. Tsai, L.-W. Kang, C.-W. Lin, and W. Lin. 2013. Scene-based movie summarization via role-community networks. IEEE T. Circ. Syst. Vid. 23, 11 (2013), 1927--1940. Google ScholarDigital Library
- H. Vala, D. Jurgens, A. Piper, and D. Ruths. 2015. Mr. Bennet, his coachman, and the Archbishop walk into a bar but only one of them gets recognized: On the difficulty of detecting characters in literary texts. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. http://piperlab.mcgill.ca/pdfs/Vala_EMNLP_2015.pdf.Google Scholar
- J. Valls-Vargas, S. Ontanón, and J. Zhu. 2014. Toward automatic character identification in unannotated narrative text. In Proceedings of the 7th Intelligent Narrative Technologies Workshop. 188--194. https://pdfs.semanticscholar.org/b755/1e222321cca2fdb1992991c434ef0aa1a2fd.pdf. Google ScholarDigital Library
- K. van Dalen-Oskam, J. de Does, M. Marx, I. Sijaranamual, K. Depuydt, B. Verheij, and V. Geirnaert. 2014. Named entity recognition and resolution for literary studies. Comput. Ling. Netherlands J. 4 (2014), 121--136. https://clinjournal.org/node/62.Google Scholar
- T. Venturini, L. Bounegru, M. Jacomy, and J. Gray. 2017. How to tell stories with networks. In Datafied Society. Amsterdam University Press. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3043857.Google Scholar
- M. Vrigkas, C. Nikou, and I. A. Kakadiaris. 2015. A review of human activity recognition methods. FRAI 2, Article 28 (2015), 28.Google Scholar
- D. J. Watts and S. H. Strogatz. 1998. Collective dynamics of “small-world” networks. Nature 393, 6684 (1998), 440--442.Google Scholar
- M. C. Waumans, T. Nicodème, and H. Bersini. 2015. Topology analysis of social networks extracted from literature. PLoS ONE 10, 6 (2015), e0126470.Google ScholarCross Ref
- C.-Y. Weng, W.-T. Chu, and J.-L. Wu. 2007. Movie analysis based on roles’ social network. In Proceedings of the ICME. 1403--1406.Google ScholarCross Ref
- C.-Y. Weng, W.-T. Chu, and J.-L. Wu. 2009. RoleNet: Movie analysis from the perspective of social networks. IEEE T. Multimed. 11, 2 (2009), 256--271. Google ScholarDigital Library
- A. Woloch. 2003. The One vs. the Many: Minor Characters and the Space of the Protagonist in the Novel. Princeton University Press. http://press.princeton.edu/titles/7622.html.Google Scholar
- A. Xanthos, I. Pante, Y. Rochat, and M. Grandjean. 2016. Visualising the dynamics of character networks. In Proceedings of the DH. 417--419. https://github.com/maladesimaginaires/intnetviz/raw/master/DH2016_xanthos_et_al.pdf.Google Scholar
- M.-C. Yeh, M.-C. Tseng, and W.-P. Wu. 2012. Automatic social network construction from movies using film-editing cues. In Proceedings of the IEEE International Conference on Multimedia and Expo Workshops. 242--247. Google ScholarDigital Library
- M.-C. Yeh and W.-P. Wu. 2014. Clustering faces in movies using an automatically constructed social network. IEEE MultiMed. 21, 2 (2014), 22--31.Google ScholarCross Ref
- C. Y. Yeung and J. Lee. 2017. Identifying speakers and listeners of speech in literary works. In Proceedings of the IJCNLP. 325--329. http://www.aclweb.org/anthology/I17-2055.Google Scholar
- M. Yeung, B.-L. Yeo, and B. Liu. 1996. Extracting story units from long programs for video browsing and navigation. In Proceedings of the 3rd lEEE International Conference Multimedia Computing 8 Systems. 296--305. Google ScholarDigital Library
- J. Yose, R. Kenna, M. MacCarron, and P. MacCarron. 2018. Network analysis of the Viking age in Ireland as portrayed in Cogadh Gaedhel re Gallaibh. Roy Soc Open Sci 5, 1 (2018), 171024.Google ScholarCross Ref
- J. Y. Zhang, A. W. Black, and R. Sproat. 2003. Identifying speakers in children’s stories for speech synthesis. In Proceedings of the Eurospeech. 2041--2044. https://www.cs.cmu.edu/ awb/papers/eurospeech2003/esper.pdf.Google Scholar
- Y.-F. Zhang, C.-S. Xu, H.Q. Lu, and Y.-M. Huang. 2009. Character identification in feature-length films using global face-name matching. IEEE T. Multimed. 11, 7 (2009), 1276--1288. Google ScholarDigital Library
Index Terms
- Extraction and Analysis of Fictional Character Networks: A Survey
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