统计文艺学：大数据时代文学研究的新范式

文艺理论研究 ›› 2016, Vol. 36 ›› Issue (5): 99-107.

统计文艺学：大数据时代文学研究的新范式

周才庶

杭州电子科技大学人文学院

出版日期:2016-09-25 发布日期:2017-09-30
作者简介:周才庶，博士，杭州电子科技大学人文学院讲师，主要从事文学理论、文化产业的研究。
基金资助:
本文系浙江省哲学社会科学规划课题《全媒体时代审美经验变迁研究》[项目编号：16NDJC050YB]、国家社会科学基金重点项目《提高我国文化软实力的中国道路研究》[项目编号：14AZD040]、中国博士后科学基金第58批面上资助《全媒体时代审美经验变迁与文学理论的新发展》[项目编号：2015M581503]的阶段性成果。

Statistical Literary Theory: New Literary Research Paradigm in the Era of Big Data

Zhou Caishu

the College of Humanities, Hangzhou Dianzi University (Hangzhou 310018, China)

Online:2016-09-25 Published:2017-09-30
About author:Zhou Caishu, Ph.D.. is a lecturer in the College of Humanities, Hangzhou Dianzi University (Hangzhou 310018, China), with research focus on literary theory.

摘要/Abstract

摘要： 大数据时代凸显了统计方法在数据处理中的作用。莫莱蒂及其斯坦福文学实验室将大量文本转换成数据，从中发现文学的普遍规律、挑战既定的文学观念，在文学研究的量化方面做出了诸多尝试。国内外学术界在文艺研究中采取统计方法已经初见端倪。从历史传统和文学研究的内在需求来看，基于数理逻辑与数据实证的文艺研究方法具有合理性。“统计文艺学”悄然兴起，它在文学研究中采取定量分析、回归分析、假设检验等统计学方法，把审美特性与科学逻辑相结合，将带来革命性的转折。

关键词: 大数据, 莫莱蒂, 定量分析, 统计文艺学

Abstract: The advent of the Big Data era highlights the importance of statistical methods. Franco Moretti and Stanford Literary Lab try to use some quantitative methods in literary researches. Their qualitative analysis on the data from books discovers some facts about literature that may challenge some established literary theories, and scholars are also trying to adopt the statistical methods to literary study in China. Statistical literary theory is literary research methodology with mathematical logic and empirical data, which comprises such methods as quantitative analysis, regression analysis, hypothesis test, and time series analysis. In term of its combination of aesthetics characteristics with scientific logic, it is revolutionary.

Key words: Big Data, Franco Moretti, quantitative analysis, statistical literary theory

周才庶. 统计文艺学：大数据时代文学研究的新范式[J]. 文艺理论研究, 2016, 36(5): 99-107.

Zhou Caishu. Statistical Literary Theory: New Literary Research Paradigm in the Era of Big Data[J]. Theoretical Studies in Literature and Art, 2016, 36(5): 99-107.

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