BG-CNN Combined with Grammatical Information for Aspect Level Sentiment Classification

doi:10.3778/j.issn.1002-8331.2009-0174

Abstract

Abstract: Aspect level sentiment analysis（ABSA） aims to determine the emotional orientation of a particular goal in a sentence.Most of the existing methods only use semantic information, and can’t make good use of the opinion terms of different aspects to achieve accurate emotion classification, and the model is not interpretable. Part of speech information in grammar level information and syntactic structure dependency tree with specific aspect words as root nodes can be used to capture opinion terms of specific aspects in sentences. Therefore, this paper proposes a BG-CNN with part of speech information and model interpretability, and introduces dependency tree as auxiliary information for fine-grained text sentiment analysis. In addition, an enhanced loss function is proposed to train the model. Experimental results on three classical datasets show the effectiveness of the model and the enhanced loss function.

Key words: aspect level sentiment analysis, dependency tree, grammatical level, model explicable

摘要： 方面级别的情感分析（ABSA）旨在确定句子中特定目标的情感倾向。大部分现有方法仅使用语义层面信息，不能很好地利用不同方面词的意见术语来达到精确的情感分类，且模型不具有可解释性。语法层面信息中词性信息和以特定方面术语为根节点的句法结构依存树可以用于捕获句子中特定方面的意见术语。提出了结合词性信息且具有模型可解释性的BG-CNN，并引入依存树作为辅助信息用于细粒度文本情感分析。提出了增强损失函数用于模型的训练。在三个经典数据集上进行验证，实验结果表明了该模型和增强损失函数的有效性。

关键词: 方面级情感分析, 依存树, 语法层面, 模型可解释性

ZHENG Cheng, WEI Suhua, CAO Yuan. BG-CNN Combined with Grammatical Information for Aspect Level Sentiment Classification[J]. Computer Engineering and Applications, 2022, 58(5): 148-155.

郑诚, 魏素华, 曹源. 结合语法信息的BG-CNN用于方面级情感分类[J]. 计算机工程与应用, 2022, 58(5): 148-155.

References

[1] MA D，LI S，ZHANG X，et al.Interactive attention networks for aspect-level sentiment classification[C]//Twenty-Sixth International Joint Conference on Artificial Intelligence，2017：4068-4074.
[2] FAN Z，WU Z，DAI X Y，et al.Target-oriented opinion words extraction with target-fused neural sequence labeling[C]//Proceedings of NAACL-HLT，2019：2509-2518.
[3] XUE W，LI T.Aspect based sentiment analysis with gated convolutional networks[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics（Long Papers），2018：2514-2523.
[4] STAUDEMEYER R C，ROTHSTEIN MORRIS E.Understanding LSTM—a tutorial into long short-term memory recurrent neural networks[J].arXiv：1909.09586，2019.
[5] WANG Y，HUANG M，ZHU X，et al.Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing，2016：606-615.
[6] TANG D，QIN B，LIU T.Aspect level sentiment classification with deep memory network[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing，2016：214-224.
[7] CHEN P，SUN Z，BING L，et al.Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing，2017：452-461.
[8] LI X，BING L，LAM W，et al.Transformation networks for target?oriented sentiment classification[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics，2018：946-956.
[9] WANG B，LU W.Learning latent opinions for aspect-level sentiment classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2018：5537-5544.
[10] SONG Y，WANG J，JIANG T，et al.Attentional encoder network for targeted sentiment classification[J].arXiv：1902.09314，2019.
[11] ZHANG C，LI Q，SONG D.Syntax-aware aspect-level sentiment classification with proximity-weighted convolution network[C]//Proceedings of the 42nd International ACM SIGIR Conference，2019：1145-1148.
[12] LEVY O，GOLDBERG Y.Neural word embedding as implicit matrix factorization[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems，2014：2177-2185.
[13] ZHENG Y，ZHANG R，MENSAH S，et al.Replicate，walk，and stop on syntax：an effective neural network model for aspect-level sentiment classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2020：9685-9692.
[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，2014：27-35.
[15] DONG L，FURN W，CHUANKI T，et al.Adaptive recursive neural network for target-dependent Twitter sentiment classification[C]//Meeting of the Association for Computational Linguistics，2014：49-54.
[16] TANG J，LU Z，SU J，et al.Progressive self-supervised attention learning for aspect-level sentiment analysis[C]//Proceedings of Annual Meeting Assoc Computer Linguistics，2019：1-10.
[17] CHENG X，XU W，WANG T，et al.Variational semi-supervised aspect-term sentiment analysis via transformer[C]//Proceedings of 23rd Conf Computer Natural Lang Learn，2019：961-969.
[18] GOULARAS D，KAMIS S.Evaluation of deep learning techniques in sentiment analysis from Twitter data[C]//Proceedings of Int Conf Deep Learn Mach Learn，2019：12-17.
[19] WANG S，MAZUMDER S，LIU B，et al.Target-sensitive memory networks for aspect sentiment classification[C]//Proceedings of 56th Meeting Assoc for Computer Linguistics，2018：957-967.
[20] TANG D，QIN B，FENG X，et al.Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of COLING，2016：3298-3307.
[21] HUANG B，CARLEY K M.Syntax-aware aspect level sentiment classification with graph attention networks[J].arXiv：1909.02606，2019.
[22] SU J，YU S，LUO D.Enhancing aspect-based sentiment analysis with capsule network[J].IEEE Access，2020：100551-100561.