[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-155534-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-155534-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","the-same-thing-only-different-classification-of-movies-by-their-story-types","The Same Thing - Only Different - Classification of Movies by their Story Types","","Story types depict how movie narratives develop through the protagonist’s character traits and the motivations that shape responses to challenges. The work defines a novel story type classification task for movies and proposes a lightweight machine learning approach. A crowdsourcing experiment labeled 45 movies by perceived story types. Movie feature extraction focuses on aspects of characters, using Decision Tree and Naive Bayes classifiers. Despite a small dataset, results substantially exceed baseline, with F1 between 0.63 and 0.77, indicating simple features can detect story-type concepts.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/the-same-thing-only-different-classification-of-movies-by-their-story-types/155534/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/the-same-thing-only-different-classification-of-movies-by-their-story-types/155534.png","ImageObject",300,407,{"name":42,"@type":43},"Dozel","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-09","2026-08-28",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",13,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main task proposed in the document?","Question",{"text":63,"@type":64},"The document defines a story type classification task for movies and frames it around how narrative structure reflects protagonist traits and motivations.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How was the labeled dataset for story types created?",{"text":68,"@type":64},"A crowdsourcing experiment labeled 45 movies according to their perceived story types.",{"name":70,"@type":61,"acceptedAnswer":71},"Which machine learning methods are used for classification and how well do they perform?",{"text":72,"@type":64},"Decision Tree and Naive Bayes classifiers are used. 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We define a novel task of story type classification of movies and propose a lightweight machine learning solution. Acrowdsourcing experiment was performed to label 45 movies for their perceived story types. We extract movie features that indicate different aspects of the movie characters and apply Decision Tree and Naive Bayes classifiers as classification algorithms. Although the labeled dataset is relatively small, the story type classification accuracy is significantly above the baseline with the F1 measure in the range of [0.63-0. 77]. The preliminary results suggest that simple movie features can be used by machine learning algorithms to detect the abstract concepts of story types.  \nKeywords: Story Analytics; Computational Narrative; Movie Under-standing; Story Type Classification  \n1. Introduction  \nBackground. While storytelling is a form of human artistic expression, we conceive of story writing and, in particular, script-writing for TV and movies as being built upon a few fairly known generative principles – a structure. Literature researchers have identified structural similarities between different stories. They claim that most stories can be attributed to a fairly small set of unique plots [3,12] about a few archetypal characters [28] . The Hollywood cliche´ is of the studio executive telling the script-writer:“Give me the same thing... only different!”  \nWe chose to build our research upon the 10 story types of [9] due to their detailed and clear definitions with plenty of examples. Revealing the high-level structure of movies (i.e., the story type and its main elements) is a major aspect of understanding the movie plots. Humans can identify and understand most of the elements of a story, such as the characters and their motivations, events and their consequences etc., and can categorize the story into one of  \nthe story types. However, current movie analytics technologies are able to detect only relatively primitive story elements, such as the human actors and some low-level actions [13, 22] . Researchers in the computational narrative understanding community have recently made progress in understanding narratives in text (books and movie scripts) [25, 24, 14, 36, 2] . To the best of our knowledge, we are the first to study story type classification for movies. Another major and common challenge faced by narrative understanding community is the collection of large-scale, reliable labeled datasets [1, 19,25], especially in the movies domain.  \nOur first objective is to provide a labeled dataset of movies [10] to facilitate the use of supervised machine learning algorithms for the problem of story types classification. A crowd-sourcing experiment is used for constructing the dataset. The collected labels are analyzed to verify the following two hypotheses: (1) most movies adhere fairly well to a general structure, described by the screenwriting book [9] and (2) even non-experts can identify those story types after watching a movie.  \nOur second objective is to provide a lightweight solution for the challenging task of story type classification, with the use of relatively simple methodology and features.  \nThe original contributions of our paper to the domain of computational narrative understanding in movies are two-fold: a) We introduce the first benchmark dataset for the problem of story type classification that will be released to the research community; and b), We demonstrate that the story type of a movie can be automatically detected using some relati","cbCaikxNE0mHzn8Q","https://ap.wps.com/l/cbCaikxNE0mHzn8Q","pdf",391654,"English","# Abstract\n# 1. Introduction\n## Background\n## Objectives and Contributions\n## Typology","[{\"question\":\"What is the main task proposed in the document?\",\"answer\":\"The document defines a story type classification task for movies and frames it around how narrative structure reflects protagonist traits and motivations.\"},{\"question\":\"How was the labeled dataset for story types created?\",\"answer\":\"A crowdsourcing experiment labeled 45 movies according to their perceived story types.\"},{\"question\":\"Which machine learning methods are used for classification and how well do they perform?\",\"answer\":\"Decision Tree and Naive Bayes classifiers are used. The reported F1 scores range from 0.63 to 0.77, which is significantly above the baseline.\"}]","The Same Thing - Only Different - Classification of Movies by their Story Types | PDF"]