Social structures and real-world incidents often influence contemporary
literary fiction. Existing research in literary fiction analysis explains these
real-world phenomena through the manual critical analysis of stories.
Conventional Natural Language Processing (NLP) methodologies, including
sentiment analysis, narrative summarization, and topic modeling, have
demonstrated substantial efficacy in analyzing and identifying similarities
within fictional works. However, the intricate dynamics of character
interactions within fiction necessitate a more nuanced approach that
incorporates visualization techniques. Character interaction graphs (or
networks) emerge as a highly suitable means for visualization and information
retrieval from the realm of fiction. Therefore, we leverage character
interaction graphs with NLP-derived features to explore a diverse spectrum of
societal inquiries about contemporary culture's impact on the landscape of
literary fiction. Our study involves constructing character interaction graphs
from fiction, extracting relevant graph features, and exploiting these features
to resolve various real-life queries. Experimental evaluation of influential
Bengali fiction over half a century demonstrates that character interaction
graphs can be highly effective in specific assessments and information
retrieval from literary fiction. Our data and codebase are available at
this https URL

利用角色互动图和自然语言处理派生的特征，对当代文学作品进行社会调查和信息检索。从文学作品构建角色互动图，提取相关图特征，并利用这些特征解决各种现实生活问题。实验评估表明，角色互动图在文学作品的特定评估和信息检索方面具有高度有效性。

利用现代文学小说中的人物交互图来理解社会结构 —— 有影响力的孟加拉作家的半个世纪编年史

Understanding Social Structures from Contemporary Literary Fiction using  Character Interaction Graph -- Half Century Chronology of Influential Bengali  Writers