[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-135919-105":59,"doc-detail-135919-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","unravelling-names-of-fictional-characters-research-paper","Unravelling Names of Fictional Characters - Research paper","","Explores whether the polarity of fictional characters’ roles (e.g., “good guy” vs “bad guy”) can be predicted from character names alone, without additional context. Builds a predictive model using phonological and related features from prior theoretical work. Evaluates feature combinations on a manually annotated corpus of characters from motion pictures, finding phonological features as most discriminative and analyzing specific phonological and phonotactic indicators.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/unravelling-names-of-fictional-characters-research-paper/135919/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/unravelling-names-of-fictional-characters-research-paper/135919.png","ImageObject",300,407,{"name":92,"@type":93},"Sarah ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-08-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the paper try to predict from character names?","Question",{"text":112,"@type":113},"It predicts the polarity of a character’s role in fiction, distinguishing positive roles (e.g., “good guy”) from negative roles (e.g., “bad guy”).","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What approach and features does the paper use?",{"text":117,"@type":113},"It uses phonological and other features proposed in prior studies of fictional names to build a predictive model.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the model evaluated and what is the main finding?",{"text":121,"@type":113},"The model is tested on a manually annotated corpus of characters from motion pictures, and experiments show phonological features are the most discriminative for role polarity.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},135919,1787330575,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143},962085320529,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Unravelling Names of Fictional Characters  \nKaterina Papantoniou  \nInstitute of Computer Science, FORTH Heraklion, Greece [papanton@ics.forth.gr](papanton@ics.forth.gr)  \nStasinos Konstantopoulos  \nInstitute of Informatics & Telecommunications, NCSR `Demokritos'Ag. Paraskevi 153 10, Athens, Greece  \n[konstant@iit.demokritos.gr](konstant@iit.demokritos.gr)  \nAbstract  \nIn this paper we explore the correlation between the sound of words and their meaning, by testing if the polarity (`good guy'or `bad guy') of a character's role in a work of fiction can be predicted by the name of the character in the absence of any other context. Our approach is based on phonological and other features proposed in prior theoretical studies of fictional names. These features are used to construct a predictive model over a manually annotated corpus of characters from motion pictures. By experimenting with different mixtures of features, we identify phonological features as being the most discriminative by comparison to social and other types of features, and we delve into a discussion of specific phonological and phonotactic indicators of a character's role's polarity.  \n1 Introduction  \nCould it be possible for fictional characters' names such as `Dr. No' and `Hannibal Lecter' to be attributed to positive characters whereas names such as `Jane Eyre' and `Mary Poppins' to negative ones? Could someone guess who is the hero and who is the competitor based only on the name of the character and what would be the factors that contribute to such intuition? Literary theory suggests that it should be possible, because fictional character names function as expressions of experience, ethos, teleology, values, culture, ideology, and attitudes of the character.  \nHowever, work in literary theory, psychology, linguistics and philosophy has studied fictional names by analysing individual works or small clusters of closely related works, such as those of a  \nparticular author. By contrast, we apply tools from computational linguistics at a larger scale aiming to identify more general patterns that are not tied to any specific creator's idiosyncrasies and preferences; in the hope that extracting such patterns can provide valuable insights about how the sound of names and, more generally, words correlates with their meaning.  \nAt the core of our approach is the idea that the names of fictional characters follow (possibly subconsciously) a perception of what a positive or a negative name ought to sound like that is shared between the creator and the audience. Naturally the personal preferences or experiences of the creator might add noise, but fictional characters' names will at least not suffer (or suffer less) from the systematic cultural bias bound to exist in real persons' names.  \nIn the remainder of this paper, we first present the relevant background, including both theoretical work and computational work relevant to peoples' names (Section 2) . Based on this theoretical work, we then proceed to formulate a set of features that can be computationally extracted from names, and which we hypothesise to be discriminative enough to allow for the construction of a model that accurately predicts whether a character plays a positive or negative role in a work of fiction (Section 3) . In order to test this hypothesis, we constructed a corpus of characters from popular English-language motion pictures. After describing corpus construction and presenting results (Section 4), we proceed to discuss these results (Section 5) and conclude (Section 6) .  \n2 Background  \n2.1 Onomastics  \nThe procedure of naming an individual, a location or an object is of particular importance and serves  \n2154  \nProceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 2154–2163, Berlin, Germany, August 7-12, 2016 . 􀀍c2016 Association for Computational Linguistics  \npurposes beyond the obvious purpose of referring to distinct entities. Characteristics such as place","cbCainAfZdMh3IGp","https://ap.wps.com/l/cbCainAfZdMh3IGp","pdf",210685,"English","# Introduction\n## Background\n### Onomastics","[{\"question\":\"What does the paper try to predict from character names?\",\"answer\":\"It predicts the polarity of a character’s role in fiction, distinguishing positive roles (e.g., “good guy”) from negative roles (e.g., “bad guy”).\"},{\"question\":\"What approach and features does the paper use?\",\"answer\":\"It uses phonological and other features proposed in prior studies of fictional names to build a predictive model.\"},{\"question\":\"How is the model evaluated and what is the main finding?\",\"answer\":\"The model is tested on a manually annotated corpus of characters from motion pictures, and experiments show phonological features are the most discriminative for role polarity.\"}]","Unravelling Names of Fictional Characters - Research paper | PDF",25]