一种基于BiGRU和胶囊网络的文本情感分析方法

doi:10.13229/j.cnki.jdxbgxb.20221229

摘要/Abstract

摘要：

针对现有基于商品评论文本的情感分析方法大都较少考虑评论文本的方面特征，相关分析模型也未同时考虑上下文长期依赖特征和文本局部特征，因而影响情感分析准确度的问题，提出了一种基于双向门控循环网络（BiGRU）和胶囊网络的文本情感分析方法。该方法首先采用基于词频统计的方法提取出评论文本的方面特征，并将其融入词向量表示中，从而有效提升词向量的表达能力。然后，采用BiGRU提取文本的上下文长期依赖特征，采用胶囊网络提取文本的局部特征，从而实现基于方面的高精度文本情感分析。真实数据集上的实验结果表明，本文提出的方法在准确率、查准率、查全率和F₁分数等评价指标上，均优于双向长短期记忆网络（BiLSTM）、CNN-LSTM、TextCNN等现有情感分析模型。

关键词: 深度学习, 方面特征, 情感分析, 评论文本

Abstract:

Most of the existing sentiment analysis methods based on commodity comments texts seldom consider the aspect features， and the related analysis models can′t consider both the long-term context dependence and the local text features， thus affecting the accuracy of sentiment analysis. To solve the problem， a sentiment analysis method based on bidirectional gated recurrent network （BiGRU） and capsule network is proposed. Firstly， the method uses the approach based on the word frequency statistics to extract the aspect features from the comment texts， and integrate them into the word vector representation to effectively improve the expression ability of the word vector. Then， the BiGRU network is used to extract the long-term context features of the texts， and Capsule Network is used to extract local features of the comment texts， thus realizing high-precision text sentiment analysis based on aspect. The experimental results on real datasets show that the proposed method is superior to bidirectional long-short term memory （BiLSTM）， CNN-LSTM， TextCNN and other sentiment classification models in Accuracy， Precision， Recall and F₁ score.

Key words: deep learning, aspect features, sentiment analysis, comment text

中图分类号:

TP311.13

乔百友,武彤,杨璐,蒋有文. 一种基于BiGRU和胶囊网络的文本情感分析方法[J]. 吉林大学学报(工学版), 2024, 54(7): 2026-2037.

Bai-you QIAO,Tong WU,Lu YANG,You-wen JIANG. A text sentiment analysis method based on BiGRU and capsule network[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(7): 2026-2037.

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参考文献 25

1	Rao Y H, Lei J S, Liu W Y,et al. Building emotional dictionary for sentiment analysis of online news[J]. World Wide Web-internet & Web Information Systems, 2014, 17(4): 723-742.
2	Wu F, Song Y, Huang Y. Microblog sentiment classification with contextual knowledge regularization[C]∥Proceedings of the AAAI Conference on Artificial Intelligence, Austin, USA, 2015: 2332-2338.
3	Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]∥Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach,USA,2017: 3859-3869.
4	Wang Y, Sun A, Han J, et al. Sentiment analysis by capsules[C]∥Proceedings of the 2018 World Wide Web Conference,Lyon, France, 2018: 1165-1174.
5	Cheng Y, Sun H, Chen H, et al. Sentiment analysis using multi-head attention capsules with multi-channel CNN and bidirectional GRU[J]. IEEE Access, 2021, 9: 60383-60395.
6	Peters M, Neumann M, Iyyer M, et al. Deep contextualized word representations[EB/OL]. [2022-07-16].
7	Li S, Zhao Z, Hu R, et al. Analogical reasoning on chinese morphological and semantic relations[EB/OL]. [2022-07-20].
8	Devlin J, Chang M W, Lee K, et al. Bert: pre-training of deep bidirectional transformers for language understanding[EB/OL]. [2022-07-21].
9	Kim Y. Convolutional neural networks for sentence classification[EB/OL]. [2022-08-09].
10	Donahue J, Anne Hendricks L, Guadarrama S, et al. Long-term recurrent convolutional networks for visual recognition and description[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 2625-2634.
11	Tan M, Santos C D, Xiang B, et al. LSTM-based deep learning models for non-factoid answer selection[EB/OL]. [2022-08-11].
12	Xiao Z, Li X, Wang L, et al. Using convolution control block for Chinese sentiment analysis[J]. Journal of Parallel and Distributed Computing, 2018, 116: 18-26.
13	Li H, Chai Y. Fine-grained sentiment analysis based on convolutional neural network[J]. Data Analysis and Knowledge Discovery, 2019, 3(1): 95-103.
14	Kumar A, Narapareddy V T, Srikanth V A, et al. Aspect-based sentiment classification using interactive gated convolutional network[J]. IEEE Access, 2020, 8: 22445-22453.
15	Cho K, Van Merriënboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[EB/OL].[2022-08-13].
16	Zhang B, Zhou W. Transformer-Encoder-GRU (T-E-GRU) for Chinese sentiment analysis on Chinese comment text[J]. Neural Processing Letters, 2023, 52(2): 1847-1867.
17	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]∥Advances in Neural Information Processing Systems,Long Beach, USA, 2017: 5998-6008.
18	Basiri M E, Nemati S, Abdar M, et al. ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis[J]. Future Generation Computer Systems, 2021, 115: 279-294.
19	Zhao W, Ye J, Yang M, et al. Investigating capsule networks with dynamic routing for text classification[EB/OL]. [2022-08-15].
20	Dong Y, Fu Y, Wang L, et al. A sentiment analysis method of capsule network based on BiLSTM[J]. IEEE Access, 2020, 8: 37014-37020.
21	Gu D, Wang J, Cai S, et al. Targeted aspect-based multimodal sentiment analysis: an attention capsule extraction and multi-head fusion network[J]. IEEE Access, 2021, 9: 157329-157336.
22	Yang K H, Liu J. Weibo sentiment analysis based on advanced capsule network[C]∥2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), Rome, Italy, 2021: 211-216.
23	Phan H T, Nguyen N T, Hwang D, et al. Aspect-level sentiment analysis using CNN over BERT-GCN[J]. IEEE Access, 2022, 10: 110402-110409.
24	Liao W, Zhou J, Wang Y, et al. Fine-grained attention-based phrase-aware network for aspect-level sentiment analysis[J]. Artificial Intelligence Review: An International Science and Engineering Journal, 2022(5): 55.
25	Huang B, Guo R, Zhu Y, et al. Aspect-level sentiment analysis with aspect-specific context position information[J]. Knowledge-Based Systems, 2022, 243: No.108473.

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情感极性	积极	中性	消极
书籍评论	10 670	4 034	3 740
手机评论	12 965	4 850	5 321
酒店评论	3 000	-	7 000

模型	Accuracy	P	R	F₁
LSTM	79.15	65.87	65.40	65.82
BiLSTM	80.53	65.67	65.01	65.33
CNN	78.35	65.57	64.97	65.27
TextCNN	80.78	66.92	66.75	66.97
CNN-LSTM	83.11	68.03	67.98	67.95
LSTM-CNN	84.57	68.89	68.74	68.46
caps-BiLSTM	85.12	68.97	69.16	69.06
ABCDM	85.03	69.29	69.37	68.32
ASP-BiGRU-CAPSULE	85.61	69.91	69.67	69.07