The application of big data analysis in studying expressions in 19th century British novels

Article type: Research Article

Authors: Liu, Yuan | Chen, Guangwei^*

Affiliations: Zhengzhou Railway Vocational and Technical College, Zhengzhou, China

Correspondence: [*] Corresponding author: Guangwei Chen, Zhengzhou Railway Vocational and Technical College, Zhengzhou, China. E-mail: chen_guangwei@outlook.com.

Abstract: With the continuous development of big data and machine learning technology, its application in literature research has gradually attracted attention. This study aims to explore how big data analysis techniques can reveal deep themes and emotional trends in 19th century British fiction. Through a comprehensive questionnaire survey, text mining and sentiment analysis, this paper studies and analyzes a large number of text data of 19th century English novels. Preliminary results show that deep neural networks and latent Dirichlet distribution (LDA) models can effectively reveal the theme and emotional changes in literary works. In addition, the analysis also reveals the literary emotional changes in 19th century English society under the background of industrialization, urbanization and other important events. Overall, this study confirms the value of big data technology in literary research and provides new perspectives and methods for future research.

Keywords: Big data, nineteenth-century English novels, emotion analysis, literary study

DOI: 10.3233/JCM-247513

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 4-5, pp. 2781-2797, 2024