基于分层注意力网络的方面情感分析

doi:10.11959/j.issn.2096-0271.2020045

Abstract

Abstract:

Aspect sentiment analysis based on deep learning is one of the hot spots in natural language processing.Aiming at aspect sentiment,a deep hierarchical attention network model based on aspect sentiment analysis was proposed.The local features of the text and the temporal relationship of different sentences were retained in model through the convolutional neural network,and the emotional features within and between sentences were obtained by using the layered long shortterm memory network (LSTM).Among them,specific aspects of information were added to LSTM and a dynamic control chain was designed to improve the traditional LSTM.A comparative experiment is conducted on the two data sets in SemEval 2014 and the Twitter data set.Compared with the traditional model,the accuracy of sentiment classification of the proposed model increases by about 3%.

Key words: deep learning, aspect sentiment, regional convolutional neural network, layered long short-term memory, attention mechanism, dynamic control chain

CLC Number:

TP391

Ting SONG, Zhanwei CHEN, Haifeng YANG. Aspect sentiment analysis based on a hierarchical attention network[J]. Big Data Research, 2020, 6(5): 82-91.

Figures/Tables 6

References 22

[1]	PANG B , LEE L . Opinion mining and sentiment analysis[J]. Foundations and Trends in Information Retrieval, 2008,2(1-2): 1-135.
[2]	KIRITCHENKO S , ZHU X D , CHERRY C ,et al. NRC-Canada-2014:detecting aspects and sentiment in customer reviews[C]// The 8th International Workshop on Semantic Evaluation.[S.l.:s.n]. 2014: 437-442.
[3]	SU J S , ZENG J L , XIONG D Y ,et al. A hierarchy-to-sequence attentional neural machine translation model[J]. IEEE/ACM Transactions on Audio Speech ＆Language Processing, 2018(99): 1-1.
[4]	YIN W P , SCHUTZE H , XIANG B ,et al. Attention-based convolutional neural network for modeling sentence pairs[J]. Transaction of the Association for Computational Linguistics, 2016,4(11): 259-272.
[5]	AL-RIFAIE M M , BISHOP J M . Swarmic sketches and attention mechanism[C]// International Conference on Evolutionary and Biologically Inspired Music and Art. Heidelberg:Springer, 2013.
[6]	LUMER E D . Selective attention to perceptual groups:the phase tracking mechanism[J]. International Journal of Neural Systems, 1992,3(1): 1-17.
[7]	Z H ANG Z , ROBINSON D , TEPPER J . Detecting hate speech on Twitter using a convolution-GRU based deep neural network[C]// ESWC 2018. Cham:Springer, 2018.
[8]	XUE W , LI T . Aspect based sentiment analysis with gated convolutional networks[C]// The 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL, 2018: 2514-2523.
[9]	XIAO R , CUI X H , ZHOU P P ,et al. LSTM based on the classification of emotion about user evaluation on shopping site[C]// 2016 International Conference on Identification,Information and Knowledge in the Internet of things. Piscataway:IEEE Press, 2016.
[10]	梁斌, 刘全, 徐进 ,等. 基于多注意力卷积神经网络的特定目标情感分析[J]. 计算机研究与发展, 2017,54(8): 1724-1735.
	LIANG B , LIU Q , XU J ,et al. Emotional score of specific target based on multi-attention convolutional neural network[J]. Computer Research and Development, 2017,54(8): 1724-1735.
[11]	WANG Y Q , HUANG M L , ZHU X Y ,et al. Attention-based LSTM for aspect-level sentiment classification[C]// The 2016 Conference on Empirical Methods in Natural Language Processing.[S.l.:s.n]. 2016: 606-615
[12]	ZHU Y H , GAO X , ZHANG W L ,et al. A bi-directional LSTM-CNN model with attention for aspect-level text classification[J]. Future Internet, 2018,10(12): 116-127.
[13]	MA Y K , PENG H Y , CAMBRIA E . Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM[C]// The 32nd AAAI Conference on Artificial Intelligence. Menlo Park:AAAI Press, 2018: 2576-5883.
[14]	TANG D U , QIN B , FENG X C ,et al. Effective LSTMs for target dependent sentiment classification[C]// The 26th International Conference on Computational Linguistics.[S.l.:s.n]. 2016: 3298-3307.
[15]	VASWANI A , SHAZEER N , PARMAR N ,et al. Attention is all you need[C]// The 31st Conference on Neural Information Processing Systems. Cambridge:MIT Press, 2017: 6000-6010.
[16]	RUDER S , GHAFFARI P , BRESLIN J G . A hierarchical model of reviews for aspectbased sentiment analysis[J]. arXiv preprint, 2016,arXiv:1609.02745.
[17]	LI D , WEI F R , TAN C Q ,et al. Adaptive recursive neural network for target-dependent Twitter sentiment classification[C]// The 52nd Annual Meeting of the Association for Computational Linguistics.[S.l:s.n]. 2014: 49-54.
[18]	KIM Y , . Convolutional neural networks for sentence classification[C]// The 2014 Conference on Empirical Methods in Natural Language Processing.[S.l.:s.n]. 2014: 1746-1751.
[19]	LIU J M , ZHANG Y . Attention modeling for targeted sentiment[C]// The 15th Conference of the European Chapter of the Association for Computational Linguistics.[S.l.:s.n]. 2017: 572-577.
[20]	WANG X , LIU Y C , SUN C J ,et al. Predicting polarities of Tweets by composing word embeddings with long short-term memory[C]// Meeting of the Association for Computational Linguistics＆ the International Joint Conference on Natural Language Processing.[S.l.:s.n]. 2015: 1343-1353.
[21]	MA D H , LI S J , ZHANG X D ,et al. Interactive attention networks for aspectlevel sentiment classification[C]// The 26th International Joint Conference on Artificial Intelligence.[S.l.:s.n]. 2017.
[22]	TAY Y , LUU A T , HUI S C . Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis[C]// National Conference on Artificial Intelligence.[S.l.:s.n]. 2018: 5956-5963.

Metrics

Recommended 0

No Suggested Reading articles found!

情感极性	Restaurant		Laptop		Twitter
情感极性	训练集/个	测试集/个	训练集/个	测试集/个	训练集/个	测试集/个
积极	2 164	728	994	341	1 561	173
消极	807	196	870	128	1 560	173
中立	637	196	464	169	3 127	346
总计	3 608	1 120	2 328	638	6 248	692

模型名称		数据集
模型名称	Restaurant	Laptop	Twitter
CNN	67.23%	59.32%	70.03%
LSTM	74.30%	66.50%	66.50%
ATT-CNN	68.19%	62.11%	72.18%
ATAE-LSTM	78.50%	69.27%	69.88%
LSTM-R	75.98%	72.59%	—
IAN	78.60%	72.10%	—
AF-LSTM	75.44%	68.81%	—
SVM	80.00%	70.49%	—
HANMAS	79.85%	75.44%	76.39%

Aspect sentiment analysis based on a hierarchical attention network

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 22

Related Articles 15

Metrics

Recommended 0

[1]	Yimin DENG, Xulong ZHANG, Shijing SI, Jianzong WANG, Jing XIAO. Human avatars synthesis technologies: a survey [J]. Big Data Research, 2023, 9(3): 114-139.
[2]	Yayun HE, Junqing PENG, Jianzong WANG, Jing XIAO. Rhythm dancer: 3D dance generation by keymotion transition graph and pose-interpolation network [J]. Big Data Research, 2023, 9(1): 23-37.
[3]	Yumeng CUI, Jingya WANG, Shangyi YAN, Zhizhong TAO. Automatic key information extraction of police records based on deep learning [J]. Big Data Research, 2022, 8(6): 127-142.
[4]	Zhitao ZHU, Shijing SI, Jianzong WANG, Jing XIAO. Survey on federated recommendation systems [J]. Big Data Research, 2022, 8(4): 105-132.
[5]	Jie WANG, Songyan ZHANG, Jiye LIANG. A semi-supervised deep learning algorithm combining consistency regularization and manifold regularization [J]. Big Data Research, 2022, 8(3): 103-114.
[6]	Kangting XU, Wei Song. A Chinese text sentiment analysis method combining language knowledge and deep learning [J]. Big Data Research, 2022, 8(3): 115-127.
[7]	Zhitao ZHAO, Lijun ZHAO, Zheng ZHANG, Ping TANG. Integration of remote sensing intelligent processing algorithm using container cloud technology [J]. Big Data Research, 2022, 8(2): 58-74.
[8]	Kai ZHANG, Yang CHE. Method of accelerating deep learning with optimized distributed cache in containers [J]. Big Data Research, 2021, 7(5): 150-163.
[9]	Jingxi WEN, Hufei YU, Jiang XIN, Yan TANG. Analysis of gender differences in the brain based on deep learning [J]. Big Data Research, 2021, 7(4): 130-140.
[10]	Xin PENG, Chi CHEN, Yun LIN. Context-based intelligent recommendation for code reuse [J]. Big Data Research, 2021, 7(1): 37-47.
[11]	Lihui WANG, Yongbin QIN. State of the art and future perspectives of the applications of deep learning in the medical image analysis [J]. Big Data Research, 2020, 6(6): 83-104.
[12]	Hufei YU, Jingxi WEN, Jiang XIN, Yan TANG. Study on domain adaptation of medical data based on generative adversarial network [J]. Big Data Research, 2020, 6(5): 45-54.
[13]	Weiliang MA, Xuan PENG, Qian XIONG, Xuanhua SHI, Hai JIN. Memory management in deep learning:a survey [J]. Big Data Research, 2020, 6(4): 56-68.
[14]	Fan YU. Research on the next-generation deep learning framework [J]. Big Data Research, 2020, 6(4): 69-80.
[15]	Luchen LIU, Jianhao SHEN, Ming ZHANG, Zichang WANG, Haoran LI, Zequn LIU. Deep learning based patient representation learning framework of heterogeneous temporal events data [J]. Big Data Research, 2019, 5(1): 25-38.