BATAE-GRU: Attention-based Aspect Sentiment Analysis Model
Authors: 
Yuan Wang, Qian WangAuthors Info & Claims
Yuan Wang, Qian WangAuthors Info & ClaimsPages 23 - 27
Abstract
Aspect-level sentiment classification aims to identify the sentiment polarity of a given aspect. However, most of the past methods do not analyze the role of words well, and the connection between context and a given aspects is not well realized, which greatly limits the effectiveness of the model. In this paper, we have designed a model based on the attention mechanism. First, the word embedding and aspect embedding are represented by pre-trained BERT coding. Next, we use the recurrent neural network to obtain the hidden state. Then, the context and aspect are related through the attention mechanism. Finally, the experiments were conducted on 3 data sets widely used in the field of sentiment analysis. The BATAE-GRU model was compared with several current advanced models. The results showed that the experimental results of the BATAE-GRU model were better than other models; Compared with the ATAE-LSTM model, the accuracy of the model in two comparative experiments has been improved by 6.9% and 9.9% respectively.
References
[1]
B. Pang and L. Lee(2007), Opinion mining and sentiment analysis, Found. Trends Inf. Retr, 2(1-2) 1–135.
[2]
B. Liu, 2012 Sentiment analysis and opinion mining, Lectures Hum. Lang. Technol, 5(1), 1–167.
[3]
L. Jiang, M. Yu, M. Zhou, X. Liu, T. Zhao. 2011 Target-dependent Twitter sentiment classification, In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. ACL, Portland, Oregon, USA.
[4]
L. Dong, F. Wei, C. Tan, 2014 Adaptive recursive neural network for target-dependent Twitter sentiment classification, In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. ACL, Stroudsburg, PA, USA.
[5]
D. Tang, B. Qin, T. Liu, and X. Feng, Effective LSTMs for target-dependent sentiment classification, https://www.aclweb.org/anthology/C16-1311/.
[6]
M. Zhang, Y. Zhang, and D.-T. Vo, 2016 Gated neural networks for targeted sentiment analysis, In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI, PA, USA.
[7]
S. Kiritchenko, X. Zhu, C. Cherry, and S. Mohammad. NRC-Canada-2014: Detecting aspects and sentiment in customer reviews, https://arxiv.org/abs/1512.01100.
[8]
R. He, W. S. Lee, H. T. Ng, and D. Dahlmeier, 2018 Effective attention modeling for aspect-level sentiment classification, In Proceedings of the 27th International Conference on Computational Linguistics. Computer, Linguistics, Santa Fe, New Mexico, USA.
[9]
J. Wang, J. Li, S. Li, Y. Kang, M. Zhang, L. Si, and G. Zhou, 2018 Aspect sentiment classification with both word-level and clause-level attention networks, In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. ACL, Stockholm, Louisiana, Sweden.
[10]
Y. Wang, M. Huang, and X. Zhu, 2016 Attention-based LSTM for Aspect-level Sentiment Classification, In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. ACL, Austin, Texas, USA.
[11]
MA D, LI S, ZHANG X, 2017 Interactive Attention Networks for Aspect-Level Sentiment Classification, In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Main track. IJCAI, San Francisco, CA, USA.
Index Terms
- BATAE-GRU: Attention-based Aspect Sentiment Analysis Model
Index terms have been assigned to the content through auto-classification.
Recommendations
Bidirectional-GRU Based on Attention Mechanism for Aspect-level Sentiment Analysis
ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and ComputingAspect-level sentiment analysis is a fine-grained natural language processing task. For traditional deep learning models, they cannot accurately construct the aspect-level sentiment features. Such as, for the sentence of "the movie is very funny, but ...
Content Attention Model for Aspect Based Sentiment Analysis
WWW '18: Proceedings of the 2018 World Wide Web ConferenceAspect based sentiment classification is a crucial task for sentiment analysis. Recent advances in neural attention models demonstrate that they can be helpful in aspect based sentiment classification tasks, which can help identify the focus words in ...
Aspect Level Sentiment Analysis with Aspect Attention
Trends and Applications in Knowledge Discovery and Data MiningAbstractAspect level sentiment classification is a fundamental task in the field of sentiment analysis, which goal is to inferring sentiment on entities mentioned within texts or aspects of them. Since it performs finer-grained analysis, aspect level ...
Comments
Information & Contributors
Information
Published In
February 2021
644 pages
ISBN:9781450389839
DOI:10.1145/3459104
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 July 2021
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ISEEIE 2021
ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering
February 19 - 21, 2021
Seoul, Republic of Korea
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 57Total Downloads
- Downloads (Last 12 months)6
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format