Aspect-level Sentiment Analysis based on BERT Fusion Multi-module

doi:10.1109/MLBDBI54094.2021.00055

	Aspect-level Sentiment Analysis based on BERT Fusion Multi-module
	Wang, WenRui1 ; Li, Qiang 2; Huang, JianMin 1; 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 2021
会议录名称	Proceedings - 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2021
页码	254-259
会议日期	December 3, 2021 - December 5, 2021
会议地点	Virtual, Online, China
出版者	Institute of Electrical and Electronics Engineers Inc.
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. © 2021 IEEE.
关键词	Classification (of information) Semantics BERT Fine grained Fine tuning Hierarchical aggregation Language model Middle layer Multimodule Semantics Information Sentiment analysis Sequence Labeling
DOI	10.1109/MLBDBI54094.2021.00055
收录类别	EI
语种	英语
EI入藏号	20221611978853
EI主题词	Sentiment analysis
EI分类号	716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 903.1 Information Sources and Analysis
原始文献类型	Conference article (CA)
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/33416
专题	信息工程与人工智能学院
作者单位	1.School Of Information Engineering, Lanzhou University Of Finance And Economics, Gansu, Lanzhou, China; 2.Key Laboratory Of Electronic Commerce, Lanzhou University Of Finance And Economics, Gansu, Lanzhou, China
推荐引用方式 GB/T 7714	Wang, WenRui,Li, Qiang,Huang, JianMin,et al. Aspect-level Sentiment Analysis based on BERT Fusion Multi-module[C]:Institute of Electrical and Electronics Engineers Inc.,2021:254-259.