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A comparative analysis of crime fiction and crime nonfiction using topic modeling
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토픽 모델링을 이용한 범죄 관련 소설과 범죄 실화의 비교 분석

논문 기본 정보

Type
Academic journal
Author
JIHYO KIM (조선대학교) Eon-Suk Ko (조선대학교)
Journal
한국언어정보학회 언어와 정보 언어와 정보 제22권 제3호 KCI Accredited Journals
Published
2018.11
Pages
109 - 134 (26page)
DOI
https://doi.org/10.29403/LI.22.3.6

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A comparative analysis of crime fiction and crime nonfiction using topic modeling
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Jihyo Kim and Eon-Suk Ko. 2018. A comparative analysis of crime fiction and crime nonfiction using topic modeling. Language and Information, 22.3, 109-134. This paper analyzes topics in crime fiction, detective fiction, mystery fiction, and crime nonfiction by applying topic modeling methods using the program R. We constructed a corpus from books related to crime in the fiction bookshelf and the crime bookshelf of Project Gutenberg. The corpus included a total of 13,119,073 words from 141 writing pieces of 56 authors. The results indicate that crime nonfiction tends to use more specific, direct, and specialized terms as compared to crime fiction. The three subcategories of crime fiction shared many topics and frequently used words expected of a novel dealing with crime, though there were subtle variations between them. We additionally compared the texts with regard to the gender of the authors. It seems that female authors of detective fiction and crime nonfiction tend to develop their stories centered around the characters, often directly referring to their names(e.g. Halsey). In comparison, male authors tend to develop the plot focusing on criminal activities and the process of resolution, and their reference to the characters often rely on relational terms(e.g. father) rather than actual names.
#corpus #topic modeling #Latent Dirichlet Allocation #Project Gutenberg

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References (16)

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김동성 / 2018 / 프로젝트 구텐베르크를 활용한 텍스트 시각화 및 용례 검색 / 언어와 정보 22(2):81~104 dbpia Blei, David / 2006 / Dynamic topic models / Proceedings of the 23rd International Conference on Machine Learning :113~120 google schola Blei, David / 2012 / Probabilistic topic models: surveying a suite of algorithms that offer a solution to managing large document archives / Communications of the ACM 55(4):77~84 google schola DiMaggio, P. / 2013 / Exploiting affinities between topic modeling and the sociological perspective on culture: application to newspaper coverage of US government arts funding / Poetics 41(6):570~606 google schola Feinerer, Ingo / tm: Text mining package. R package version 0.7-5 google schola

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