Aiming at the problem of information loss in encoding long-distance texts as using recurrent neural networks, and the problem of attention bias to the high-frequency sentiment information when using the attention mechanism to extract sentiment information, this paper proposes a method of aspect-level sentiment classification based on multi-pattern feature fusion. The method divides sentiment information into three categories:single-point sentiment information, multi-point sentiment information and partial sentiment information with different expression patterns, and realizes mutual confirmation and error correction among various types of features by focusing on, extracting and fusing the three types of emotion information. The problems of information loss and attention bias are reduced, and the ability of aspect-level sentiment classification under complex sentiment expression patterns is enhanced. Experimental results show that the accuracy and F1 value of the aspect-level sentiment classification task in extracting and fusing sentiment information can be significantly improved by using the proposed method.
FAN Shouxiang, YAO Junping, LI Xiaojun, CHENG Kaiyuan
. A Method for Aspect-Level Sentiment Classification Based on Multi-pattern Feature Fusion[J]. Journal of Applied Sciences, 2021
, 39(6)
: 969
-982
.
DOI: 10.3969/j.issn.0255-8297.2021.06.008
[1] Chen P, Sun Z, Bing L, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//EMNLP 2017-Conference on Empirical Methods in Natural Language Processing, 2017:452-461.
[2] Tang J, Lu Z, Su J, et al. Progressive self-supervised attention learning for aspect-level sentiment analysis[C]//ACL 2019-57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2020:557-566.
[3] Dong L, Wei F, Tan C, et al. Adaptive recursive neural network for target-dependent Twitter sentiment classification[C]//The 52nd Annual Meeting of the Association for Computational Linguistics, 2014, 2:49-54.
[4] Tang D, Qin B, Feng X, et al. Effective LSTMs for target-dependent sentiment classification[C]//COLING 2016-26th International Conference on Computational Linguistics, 2016:3298- 3307.
[5] Xue W, Li T. Aspect based sentiment analysis with gated convolutional networks[C]//ACL 2018-56th Annual Meeting of the Association for Computational Linguistics, 2018, 1:2514- 2523.
[6] Wang B, Lu W. Learning latent opinions for aspect-level sentiment classification[C]//The 32nd AAAI Conference on Artificial Intelligence, 2018:5537-5544.
[7] Tang D, Qin B, Liu T. Aspect level sentiment classification with deep memory network[C]//EMNLP 2016-Conference on Empirical Methods in Natural Language Processing, 2016:214-224.
[8] 曾义夫, 蓝天, 吴祖峰, 等. 基于双记忆注意力的方面级别情感分类模型[J]. 计算机学报, 2019, 42(8):1845-1857. Zeng Y F, Lan T, Wu Z F, et al. Bi-memory based attention model for aspect level sentiment classification[J]. Chinese Journal of Computers, 2019, 42(8):1845-1857. (in Chinese)
[9] Ma D, Li S, Zhang X, et al. Interactive attention networks for aspect-level sentiment classification[C]//IJCAI International Joint Conference on Artificial Intelligence, 2017:4068-4074.
[10] 支淑婷, 李晓戈, 王京博, 等. 基于多注意力长短时记忆的实体属性情感分析[J]. 计算机应用, 2019, 39(1):160-167. Zhi S T, Li X G, Wang J B, et al. Sentiment analysis of entity aspects based on multiattention long short-term memory[J]. Journal of Computer Applications, 2019, 39(1):160-167. (in Chinese)
[11] 刘全, 梁斌, 徐进, 等. 一种用于基于方面情感分析的深度分层网络模型[J]. 计算机学报, 2018, 41(12):3-18. Liu Q, Liang B, Xu J, et al. A deep hierarchical neural network model for aspect-based sentiment analysis[J]. Chinese Journal of Computers, 2018, 41(12):3-18. (in Chinese)
[12] Ruder S, Ghaffari P, Breslin J G. A hierarchical model of reviews for aspect-based sentiment analysis[C]//EMNLP 2016-Conference on Empirical Methods in Natural Language Processing, 2016:999-1005.
[13] Wang J, Li J, Li S, et al. Aspect sentiment classification with both word-level and clause-level attention networks[C]//IJCAI International Joint Conference on Artificial Intelligence, 2018:4439-4445.
[14] Pontiki M, Galanis D, Pavlopoulos J, et al. SemEval-2014 task 4:aspect-based sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, 2014:27-35.
[15] Bird S. NLTK:the natural language toolkit[C]//Proceedings of the COLING/ACL on Interactive Presentation Sessions, 2006:69-72.
[16] Manning C, Surdeanu M, Bauer J, et al. The stanford CoreNLP natural language processing toolkit[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics:System Demonstrations, 2014:55-60.
[17] Kingma D P, Ba J L. Adam:a method for stochastic optimization[C]//The 3rd International Conference on Learning Representations, 2015:https://arxiv.org/abs/1412.6980v8.
[18] Song Y, Wang J, Jiang T, et al. Targeted sentiment classification with attentional encoder network[C]//Lecture Notes in Computer Science, 2019.
[19] Wang Y, Huang M, Zhao L, et al. Attention-based LSTM for aspect-level sentiment classification[C]//EMNLP 2016-Conference on Empirical Methods in Natural Language Processing, 2016:606-615.
[20] He R, Lee W S, Ng H T, et al. Effective attention modeling for aspect-level sentiment classification[C]//Proceedings of the 27th International Conference on Computational Linguistics, 2018:1121-1131.
[21] He R, Lee W S, Ng H T, et al. Exploiting document knowledge for aspect-level sentiment classification[C]//ACL 2018-56th Annual Meeting of the Association for Computational Linguistics, 2018, 2:579-585.
[22] Chen Z, Qian T. Transfer capsule network for aspect level sentiment classification[C]//ACL 2019-57th Annual Meeting of the Association for Computational Linguistics, 2020:547-556.
