A spect-level Slentiment Analysis based on BERT Fusion Multi-module

	A spect-level Slentiment Analysis based on BERT Fusion Multi-module
	Wang, WenRui1 ; Li, Qiang 1,2; Huang, JianMin1 ; Wang, XueRong1 ; Zhao, Jin Yu 1; Li, CongCong1
	2021
会议名称	3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)
会议录名称	IEEE
页码	254-259
会议日期	DEC 03-05, 2021
会议地点	ELECTR NETWORK
出版地	NEW YORK
摘要	Aspect-level sentiment analysis is a fine-grained sentiment analysis task. Its task is to calculate people's opinions, evaluations, attitudes and emotions expressed by entities. In recent years, many deep language models have made great progress in this regard, including BERT. The initial layer to the middle layer of BERT can extract grammatical information, but the semantic information of higher layers is often easily ignored. Because extracting sentence sentiment is based on semantics, this article adds two modules of parallel aggregation and hierarchical aggregation on the basis of BERT. Parallel aggregation is used for aspect extraction, and hierarchical aggregation is used for aspect sentiment classification tasks, and the conditional random field is used as Sequence marking tasks to extract more semantic information. From the experimental results on the SemEval 2014 and SemEval 2016 data sets, it can be seen that the accuracy and F1 value of the model proposed in this paper are better than the comparison model, confirming the effectiveness of the model.
关键词	BERT sequencelabeling sentiment analysis fine-tuning
收录类别	CPCI-S
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS记录号	WOS:000804043100048
原始文献类型	Proceedings Paper
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/34572
专题	信息工程与人工智能学院
作者单位	1.Lanzhou Univ Finance & Econ, Sch Informat Engn, Lanzhou, Gansu, Peoples R China; 2.Lanzhou Univ Finance & Econ, Key Lab Elect Commerce, Lanzhou, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Wang, WenRui,Li, Qiang,Huang, JianMin,et al. A spect-level Slentiment Analysis based on BERT Fusion Multi-module[C]. NEW YORK,2021:254-259.