Chinese text sentiment analysis based on joint network and attention model

WANG Li-ya; LIU Chang-hui; CAI Dun-bo; ZHAO Tong-zhou; WANG Meng

Chinese

您当前的位置：

首页 >

文章列表页 >

Chinese text sentiment analysis based on joint network and attention model

更新时间：2026-09-04

- Chinese text sentiment analysis based on joint network and attention model
- Vol. 37, Issue 1, Pages: 80-86(2020)
- 作者机构：
  
  武汉工程大学计算机科学与工程学院,湖北,武汉,430205
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2020
- 稿件说明：
移动端阅览
王丽亚, 刘昌辉, 蔡敦波, 赵彤洲, 王梦. 基于字符级联合网络特征融合的中文文本情感分析[J]. 微电子学与计算机, 2020, 37(1): 80-86.

WANG Li-ya, LIU Chang-hui, CAI Dun-bo, et al. Chinese text sentiment analysis based on joint network and attention model[J]. 2020, 37(1): 80-86.
王丽亚, 刘昌辉, 蔡敦波, 赵彤洲, 王梦. 基于字符级联合网络特征融合的中文文本情感分析[J]. 微电子学与计算机, 2020, 37(1): 80-86. DOI：

WANG Li-ya, LIU Chang-hui, CAI Dun-bo, et al. Chinese text sentiment analysis based on joint network and attention model[J]. 2020, 37(1): 80-86. DOI：

摘要

针对传统卷积神经网络(CNN)同层神经元之间信息不能互传，无法充分利用同一层次上的特征信息，以及无法提取长距离上下文相关特征的问题.该文针对中文文本，提出字符级联合网络特征融合的模型进行情感分析，在字符级的基础上采用BiGRU和CNN-BiGRU并行的联合网络提取特征，利用CNN的强学习能力提取深层次特征，再利用双向门限循环神经网络(BiGRU)进行深度学习，加强模型对特征的学习能力.另一方面，利用BiGRU提取上下文相关的特征，丰富特征信息.最后在单方面上引入注意力机制进行特征权重分配，降低噪声干扰.在数据集上进行多组对比实验，该方法取得92.36%的F1值，结果表明本文提出的模型能有效的提高文本分类的准确率.

Abstract

For the traditional convolutional neural network (CNN)

the information between the same layer of neurons cannot be transmitted to each other

and the feature information at the same level cannot be fully utilized

and the problem of long-distance context-related features cannot be extracted. In this paper

based on the Chinese text

this paper proposes a model of character-level joint network feature fusion for sentiment analysis. Based on the character level

BiGRU and CNN-BiGRU parallel joint network are used to extract features

and CNNs strong learning ability is used to extract deep features and reuse. The two-way threshold cyclic neural network (BiGRU) performs deep learning and enhances the models ability to learn features. On the other hand

BiGRU is used to extract context-related features to enrich feature information. Finally

the attention mechanism is introduced unilaterally to perform feature weight distribution to reduce noise interference. Multi-group comparison experiments were carried out on the dataset. The method obtained 92.36% F1 value. The results show that the proposed model can effectively improve the accuracy of text classification.

关键词

Keywords

references

HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Computation, 1997(9): 1735-1780. DOI: 1735

MNIH V, HEESS N, GRAVES A. Recurrent models of visual attention[C]//Advances in Neural Information Processing Systems.[S.L].2014: 2204-2212.

王煜涵, 张春云, 赵宝林, 等.卷积神经网络下的Twitter文本情感分析[J].数据采集与处理, 2018, 33(5):921-927. DOI: 921

BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[J]. Computer Science, 2014, 10(4): http://arxiv.org/abs/1409.0473.

MERRIENBOER B V, GULCEHRE C. Learning phrase representions using RNN Encoder Decoder for statistical machine translation [C] //Computer Science 2014, [S.L].2014: 1 -12.

LIU B. Sentiment analusis and opinion mining[C]//Proceedings of the 2012 Synthesis Lectures on Human Language Technologies. Vermont, Australia: Morgan % Claypool Publishers. 2012: 152-153.

KIM Y. Convolutional neural networks for sentence classification[J]. Eprint Arxiv, arxiv.org/abs/1408.5882, 2014.

EBRAHIMI J, DOU D. Chain based RNN for relation classification [C] //Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.Denver, USA, 2015: 1244 -1249.

ZHOU C, SUN C, LIU Z, et al. A C-LSTM Neural Network for Text Classification[J]. Computer Science, 2015, 1(4): 39-44. DOI: 39

付广成.基于深度神经网络的电商评论情感分析研究[D].南昌: 南昌大学, 2018.

任勉, 甘刚.基于双向LSTM模型的文本情感分类[J].计算机工程与设计, 2018, 39(7):2064-2068. DOI: 2064

MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed Representations of Words and Phrases and their Compositionality[J]. Advances in Neural Information Processing Systems, 2013(26):3111- 3119. DOI: 3111

MIKOLOV T, CHEN K, CORRADO G, et al. Efficient Estimation of Word Representations in Vector Space[J]. Computer Science, 2013.arXiv: 1301.3781v3.

孙志军, 薛磊, 许阳明, 等.深度学习研究综述[J].计算机应用研究, 2012, 29(8):2806-2810. DOI: 2806

MNIH A, HINTON G E. A scalable hierarchical distributed language model[C]//Proceedings of the 22nd Annual Conference on Neural Information Processing, Istanbul, 2008. New York: NIPS, 2009: 1081-1088.

KALCHBRENNER N, GREFENSTETTE E, BLUNSOM P.A convolutional neural network for modelling sentences[J]. arXiv: 1404.2188v1.

魏韡, 向阳, 陈千.中文文本情感分析综述[J].计算机应用, 2011, 31(12):3321-3323. DOI: 3321

BENGIO Y, SCHWENK H, SENéCAL J S, et al. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2003, 3(6):1137-1155. DOI: 1137

CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language. Doha: CoRR, 2014: 1724-1734.

曹宇慧.基于深度学习的文本情感分析研究[D].哈尔滨: 哈尔滨工业大学, 2016.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on network anomaly detection based on TCN-BiGRU

A spherical perception-based panoramic image saliency prediction algorithm with longitude weighting

CNN inference task division and memory scheduling algorithm based on tiling subgraph

Design of PCNN_LSTM hardware accelerator for SOC estimation of lithium batteries

Research on SAR image denoising technology based on U-Net network structure

Related Author

WANG Qikai

CAO Rushang

YIN Chunyong

CHEN Ziyi

LU Chenglong

LAN Hong

GE Mengke

CHENG Ran

Related Institution

College of Software Engineering, Chengdu University of Information Technology

School of Computer Science, School of Cyber Science and Engineering, Nanjing University of Information Science & Technology

School of Information Engineering, Jiangxi University of Science and Technology

School of Microelectronics, University of Science and Technology of China

Institute of Artificial Intelligence, Hefei Comprehensive National Science Center

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰