[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-269479-105":59,"doc-detail-269479-en":131},{"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":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","personality-profiling-of-fictional-characters-using-sense-level-links-between-lexical-resources-paper","Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources - paper","","The study investigates automated personality prediction for protagonists in novels using the Five-Factor Model. It introduces and publishes a collaboratively built dataset of fictional character personality and frames the task as text classification. Semantic features are derived from WordNet and VerbNet sense-level information together with word vector representations. Three machine-learning models are evaluated from speech, actions, and predicatives, with lexical-semantic features outperforming baselines most strongly, matching known psychology findings.",{"@graph":69,"@context":123},[70,84,106],{"@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/personality-profiling-of-fictional-characters-using-sense-level-links-between-lexical-resources-paper/269479/",{"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/personality-profiling-of-fictional-characters-using-sense-level-links-between-lexical-resources-paper/269479.png","ImageObject",300,407,{"name":92,"@type":93},"Stanley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What personality framework does the study use for fictional character profiling?","Question",{"text":113,"@type":114},"The study uses the Five-Factor Model (FFM), treating personality as five traits along bipolar scales.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is the personality prediction problem formulated in the paper?",{"text":118,"@type":114},"It is framed as a text classification task using features extracted from the protagonists’ textual evidence.",{"name":120,"@type":111,"acceptedAnswer":121},"Which semantic features provide the strongest results?",{"text":122,"@type":114},"Lexical-semantic features based on sense-level information from WordNet and VerbNet, together with word vector representations, significantly outperform baselines, especially for direct speech and actions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},269479,1790468441,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":41,"preview_page_count":4},2336477405376,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Personality Profiling of Fictional Characters using Sense-Level Links  \nbetween Lexical Resources  \nLucie Flekovay and Iryna Gurevychyz  \ny Ubiquitous Knowledge Processing Lab (UKP-TUDA) Department of Computer Science, Technische Universitt Darmstadt z Ubiquitous Knowledge Processing Lab (UKP-DIPF) German Institute for Educational Research  \n[www.ukp.tu-darmstadt.de](www.ukp.tu-darmstadt.de)  \n“Always be yourself, unless you can be Batman. Then always be Batman.”  \n– Bill Murray  \nAbstract  \nThis study focuses on personality prediction of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character personality and design our task as a text classification problem. We incorporate a range of semantic features, including WordNet and VerbNet sense-level information and word vector representations. We evaluate three machine learning models based on the speech, actions and predicatives of the main characters, and show that especially the lexical-semantic features significantly outperform the baselines. The most predictive features correspond to reported findings in personality psychology.  \n1 Introduction  \nRecent progress in NLP has given rise to the field of personality profiling-automated classification of personality traits based on written, verbal and multimodal behavior of an individual. This research builds upon findings from classical personality psychology and has applications in a wide range of areas from medicine (suicide prevention) across security (forensics, paedophile detection, cyberbullying) to marketing and sales (recommendation systems, target group profiles) . The gold standard labels for an objective evaluation of personality are mostly obtained by means of personality tests of the Five Factor Model (FFM) (McCrae and Costa, 1987; Goldberg, 1990), which is wellknown and widely accepted in psychology and other research fields. The FFM defines personality  \nalong five bipolar scales: Extraversion (sociable vs. reserved), Emotional stability (secure vs. neurotic), Agreeableness (friendly vs. unsympathic), Conscientiousness (organized vs. careless) and Openness to experience (insightful vs. unimaginative) . Psychologists have shown that these five personality traits are stable across individual lifespan, demographical and cultural differences (John and Srivastava, 1999) and affect many life aspects.(Terracciano et al., 2008; Rentfrow et al., 2011) .  \nIt has been shown that the personality traits of readers impact their literature preferences (Tirre and Dixit, 1995; Mar et al., 2009) . Psychology researchers also found that perceived similarity is predictive of interpersonal attraction (Montoya et al., 2008; Byrne, 1961; Chartrand and Bargh, 1999) . More explicitly, recent research (Kaufman and Libby, 2012) shows that readers of a narrative develop more favorable attitudes and less stereotype application towards a character, if his difference (e.g. racial) is revealed only later in the story. We therefore hypothesize that readers might have a preference for reading novels depicting fictional characters that are similar to themselves. Finding a direct link between reader's and protagonist's personality traits would advance the development of content-based recommendation systems. As a first step to explore this hypothesis further, it needs tobe determined if we are able to construct a personality profile of a fictional character in a similar way as it is done for humans, and which aspects of personality profiling can be exploited to automatize such procedure.  \nIn this paper, we open this research topic by presenting a novel collaboratively built dataset of fictional character personality in Section 3, which we make available on our website.1 Framing the personality prediction as a text classification task, we incorporate features of both lexical-  \n1 [https://www.ukp.tu-darmstadt.de/data/](https://www.ukp.tu-darmstadt.de/data/)[ ](https://www.ukp.t","cbCaipUxRDRIQaTp","https://ap.wps.com/l/cbCaipUxRDRIQaTp","pdf",401527,12,"English","# Introduction\n## Personality profiling as NLP task\n# Related work\n## Content-based recommendation and semantic information\n## Entity-centric and latent character models","[{\"question\":\"What personality framework does the study use for fictional character profiling?\",\"answer\":\"The study uses the Five-Factor Model (FFM), treating personality as five traits along bipolar scales.\"},{\"question\":\"How is the personality prediction problem formulated in the paper?\",\"answer\":\"It is framed as a text classification task using features extracted from the protagonists’ textual evidence.\"},{\"question\":\"Which semantic features provide the strongest results?\",\"answer\":\"Lexical-semantic features based on sense-level information from WordNet and VerbNet, together with word vector representations, significantly outperform baselines, especially for direct speech and actions.\"}]","Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources - paper | PDF",1789452317]