融合双通道的语义信息的方面级情感分析
Aspect-level sentiment analysis fusing dual-channel semantic information
廖列法 1张文豪2
作者信息
- 1. 江西理工大学软件工程学院,江西南昌 330000;江西理工大学信息工程学院,江西赣州 341000
- 2. 江西理工大学信息工程学院,江西赣州 341000
- 折叠
摘要
针对方面级情感分析任务中语义信息难以提取以及方面词信息难以和上下文信息相关联的问题,提出一种融合双通道的语义信息模型(FDCS).通过BERT预训练模型搭建两个通道获取不同层次的语义信息,一个是全局信息通道,另一个是句子信息通道;使用语义注意力融合双通道中不同层次的语义信息,将融合后的语义信息再次分别融入全局信息和句子信息;根据每个通道语义信息的不同分别提取相应的特征信息.在3个基准数据集上的实验结果表明,该模型的性能优于其它模型.
Abstract
Aiming at the problems that semantic information is difficult to extract,and that aspect word information is difficult to associate with context information in aspect-level sentiment analysis tasks,a fused dual-channel semantic information model(FDCS)was proposed.Two channels were built through the BERT pre-training model to obtain semantic information at diffe-rent levels,one was the global information channel,and the other was the sentence information channel.Semantic attention was used to fuse the semantic information of different levels in the dual channels,and the fused semantic information was re-integrated into the global information and sentence information respectively.The corresponding feature information was extracted according to the different semantic information of each channel.Experimental results on three benchmark datasets show that this model outperforms other models.
关键词
方面级情感分析/方面词/预训练模型/双通道/语义信息/语义注意力/特征信息Key words
aspect-level sentiment analysis/aspect word/pre-training model/dual-channel/semantic information/semantic attention/feature information引用本文复制引用
基金项目
国家自然科学基金项目(71462018)
国家自然科学基金项目(71761018)
出版年
2024
NETL
NSTL
万方数据