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Design and implementation of classical literature sentiment analysis system based on ensemble learning and graph neural network

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Bibliographic Details
Title: Design and implementation of classical literature sentiment analysis system based on ensemble learning and graph neural network
Authors: Qianru Gao, Jiachen Huang
Source: International Journal of Cognitive Computing in Engineering, Vol 6, Iss, Pp 603-616 (2025)
Publisher Information: Elsevier BV, 2025.
Publication Year: 2025
Subject Terms: System design, Ensemble learning, Electronic computers. Computer science, Science, Classical literature sentiment analysis, QA75.5-76.95, Graph neural network
Description: Classical literary works have attracted extensive attention in modern society because of their unique cultural memory and aesthetic value. However, due to the long history and evolution of language, how to accurately grasp its connotation, especially its emotional color, has always been a dilemma for researchers. This study is committed to the design and implementation of a classical literature sentiment analysis system based on ensemble learning and graph neural network, with the goal of breaking through the limitations of traditional methods and realizing the refined analysis of classical literature sentiment tendencies. By constructing a large-scale corpus covering classics from different eras, this study lays a solid data foundation for model training. Graph neural network technology is innovatively applied to sentiment analysis in classical literature, and the graph structure composed of lexical nodes and semantic edges is used to capture the deep semantic and structural connections of texts. At the same time, bagging and boosting ensemble learning strategies are introduced to optimize the performance of multiple GNN models and form a more robust decision set. Experimental results show that compared with traditional methods, the graph neural network has an accuracy of 91.5 % for sentiment classification, and the ensemble learning further reduces the false positive rate, improving the overall emotion recognition accuracy of the system to 93.7 %, providing an efficient and accurate innovative solution for sentiment analysis of classical literature.
Document Type: Article
Language: English
ISSN: 2666-3074
DOI: 10.1016/j.ijcce.2025.05.004
Access URL: https://doaj.org/article/7bd1281e688b49919b3dfea013ca509e
Rights: CC BY NC ND
Accession Number: edsair.doi.dedup.....4bd63e33a49d624ac36c780511588e0a
Database: OpenAIRE
Description
Abstract:Classical literary works have attracted extensive attention in modern society because of their unique cultural memory and aesthetic value. However, due to the long history and evolution of language, how to accurately grasp its connotation, especially its emotional color, has always been a dilemma for researchers. This study is committed to the design and implementation of a classical literature sentiment analysis system based on ensemble learning and graph neural network, with the goal of breaking through the limitations of traditional methods and realizing the refined analysis of classical literature sentiment tendencies. By constructing a large-scale corpus covering classics from different eras, this study lays a solid data foundation for model training. Graph neural network technology is innovatively applied to sentiment analysis in classical literature, and the graph structure composed of lexical nodes and semantic edges is used to capture the deep semantic and structural connections of texts. At the same time, bagging and boosting ensemble learning strategies are introduced to optimize the performance of multiple GNN models and form a more robust decision set. Experimental results show that compared with traditional methods, the graph neural network has an accuracy of 91.5 % for sentiment classification, and the ensemble learning further reduces the false positive rate, improving the overall emotion recognition accuracy of the system to 93.7 %, providing an efficient and accurate innovative solution for sentiment analysis of classical literature.
ISSN:26663074
DOI:10.1016/j.ijcce.2025.05.004