文献类型：期刊文献

英文题名：Design and Development of Emotional Analysis System for Chinese Online Comment Text

作者：Yao, Jingxi[1]; Hu, Qingchun[1]; Zhou, Tianyi[1]; Wang, Yilin[1]

机构：[1] East China University of Science and Technology, School Ot Intormation Science and Engineering, Shanghai, 200237, China

年份：2023

起止页码：237

外文期刊名：2023 4th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

收录：EI(收录号：20233214493048)

语种：英文

外文关键词：Data mining - Deep learning - E-learning - Learning systems

摘要：Performing sentiment analysis on a massive volume of online comments data has significant commercial value. Therefore, this paper proposes a sentiment analysis platform focusing on Chinese comment text. In system design, we utilized deep learning techniques and implemented a large-scale pre-trained language model ERNIE for text feature extraction. We fine-tuned the network structure targeting downstream tasks to achieve Chinese sentiment analysis, overcoming the challenges of traditional machine learning methods, such as low accuracy and scalability. Additionally, we compared the experimental results with prominent pre-trained models, analyzed and evaluated the experimental data. The experimental results indicate that the platform can enhance the data analytic capabilities of online Chinese comment text, ameliorate the communication efficiency between industry audiences and creators, and extract commercial values from Internet comments. ? 2023 IEEE.