Classification and Pattern Extraction of Stories Rated as " Tearful" Based on Narrative Structure Analysis
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- FUKUMOTO Takaki
- Future University Hakodate
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- SHIRATORI Takayuki
- Future University Hakodate
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- TOYOSAWA Shuuhei
- Future University Hakodate
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- YOSHIDA Takumi
- Future University Hakodate
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- ISHIKAWA Kazuki
- Future University Hakodate
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- IWASAKI Junya
- Future University Hakodate
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- SAITO Yuuri
- Future University Hakodate
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- NAKAMURA Shougo
- Future University Hakodate
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- OHTA Shoki
- Future University Hakodate
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- OHBA Arisa
- Future University Hakodate
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- MURAI Hajime
- Future University Hakodate
Bibliographic Information
- Other Title
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- 物語構造分析に基づく「泣ける」と評価される物語の分類とパターン抽出
Abstract
<p>In recent years, automatic generation based on narrative structure analysis has been realized in various narrative genres. However, there have been few attempts at structural analysis and automatic generation of "tearful" narratives that move readers to tears. In this study, the narrative features necessary for the automatic generation of "tear-jerking" stories was extracted. First, the stories that were evaluated as "tearful" by many people based on the voting sites on the Web were selected. Next, structural analysis for selected narrative works and categorization of tear-inducing techniques for "tear-inducing" scenes were conducted. In addition, extraction the characteristics of "tearful" stories by comparing the selected stories with general works of the same genre was attempted. As a result, it was found that the "tearful" scene tended to be associated with the presentation of hidden information by the characters and the expression of determination to overcome difficulties. It is thought that a structure that induces empathy in the user is important for a " crying" story. It is expected that the results of this research can be applied to the automatic generation of narratives to create "tear-jerking" stories.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2022 (0), 1H5OS17b02-1H5OS17b02, 2022
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390011231125693568
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed