Aspect-level Sentiment Classification Based on Double Channel Semantic Difference Network
ZENG Biqing1, XU Mayi1, YANG Jianhao1, PEI Fenghua 1, GAN Zibang1, DING Meirong 1, CHENG Lianglun2
1.School of Software, South China Normal University, Foshan, Guangdong 528225, China; 2.Guangdong Provincial Key Laboratory of Cyber-Physical Systems, Guangzhou, Guangdong 510006, China
Abstract：Aspect-Level sentiment classification aims to analyze the sentiment polarity of different aspect words in a sentence. To realize aspect-word aware contextual representations, this paper proposes a double channel semantic difference network(DCSDN) with the notation of theory of Semantic Difference. The DCSDN captures the contextual feature information of different aspects in the same text with the double channel architecture, and extract the semantic features of the texts in the double channel via a semantic extraction network. It employs the semantic difference attention to enhance the attention to key information. Experiments on Laptop datasets and Restaurant datasets (SemEval2014) and the Twitter dataset(ACL) demonstrate the accuracy reaching 81.35%, 86.34% and 78.18% respectively.
[1] Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: a survey[J]. Ain Shams Engineering Journal, 2014, 5(4): 1093-1113. [2] Zhang L, Wang S, Liu B. Deep learning for sentiment analysis: a survey[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018, 8(4): e1253. [3] Pontiki M, Galanis D, Papageorgiou H, et al. SemEval-2016 task 5: Aspect based sentiment analysis[C]//Proceedings of the 10th International Workshop on Semantic Evaluation. Stroudsburg, PA: ACL, 2016: 19-30. [4] Pontiki M, Papageorgiou H, Galanis D, et al. SemEval-2014 task 4: Aspect based sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation. Stroudsburg, PA: ACL, 2014: 27-35. [5] Liu B. Sentiment analysis and opinion mining[J]. Synthesis Lectures on Human Language Technologies, 2012, 5(1): 1-167. [6] 朱张莉, 饶元, 吴渊, 等. 注意力机制在深度学习中的研究进展[J]. 中文信息学报,2019, 33(6): 1-11. [7] LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series[J]. The Handbook of Brain Theory and Neural Networks, 1995, 3361(10): 1995. [8] Cho K, van Merrinboer 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 Processing. Stroudsburg, PA: ACL, 2014: 1724-1734. [9] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. [10] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv: 14090473,2014, [11] Ma D, Li S, Zhang X, et al. Interactive attention networks for aspect-level sentiment classification[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Menlo Park, CA: AAAI, 2017: 4068-4074. [12] Fan F, Feng Y, Zhao D. Multi-grained attention network for aspect-level sentiment classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2018: 3433-3442. [13] Huang B, Ou Y, Carley K M. Aspect level sentiment classification with attention-over-attention neural networks[C]//Proceedings of International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. Berlin: Springer, 2018: 197-206. [14] Yang C, Zhang H, Jiang B, et al. Aspect-based sentiment analysis with alternating coattention networks[J]. Information Processing & Management, 2019, 56(3): 463-478. [15] Song Y, Wang J, Jiang T, et al. Targeted sentiment classification with attentional encoder network[C]//International Conference on Artificial Neural Networks. Berlin: Springer, 2019: 93-103. [16] 杜成玉, 刘鹏远. 基于螺旋注意力网络的方面级别情感分析模型[J]. 中文信息学报,2020, 34(9): 70-77. [17] Fan Z, Wu Z, Dai X, et al. Target-oriented opinion words extraction with target-fused neural sequence labeling[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, PA: ACL, 2019: 2509-2518. [18] 梅莉莉, 黄河燕, 周新宇, 等. 情感词典构建综述[J]. 中文信息学报, 2016, 30(5): 19-27. [19] Pang B, Lee L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, Stroudsburg, PA: ACL, 2002: 79-86. [20] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. [21] Dong L, Wei F, Tan C, et al. Adaptive recursive neural network for target-dependent twitter sentiment classification[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2014: 49-54. [22] Ruder S, Ghaffari P, Breslin J G. A hierarchical model of reviews for aspect-based sentiment analysis[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2016: 999-1005. [23] Tang D, Qin B, Liu T. Aspect level sentiment classification with deep memory network[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2016: 214-224. [24] Ma Y, Peng H, 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, CA: AAAI, 2018: 5876-5883. [25] 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, Stroudsburg, PA: ACL, 2018: 946-956. [26] Chen Z, Qian T. Transfer capsule network for aspect level sentiment classification[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Stroudsburg, PA: ACL, 2019: 547-556. [27] Du C, Sun H, Wang J, et al. Capsule network with interactive attention for aspect-level sentiment classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Stroudsburg, PA: ACL, 2019: 5492-5501. [28] Jiang Q, Chen L, Xu R, et al. A challenge dataset and effective models for aspect-based sentiment analysis[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing,Stroudsburg,PA: ACL,2019: 6281-6286. [29] Wang J, Sun C, Li S, et al. Aspect sentiment classification towards question-answering with reinforced bidirectional attention network[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Stroudsburg, PA: ACL, 2019: 3548-3557. [30] Chen Z, Qiuchi L, Dawei S. Aspect-based sentiment classification with aspect-specific graph convolutional networks[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Stroudsburg, PA: ACL, 2019: 4568-4578. [31] He R, Lee W S, Ng H T, et al. An interactive multi-task learning network for end-to-end aspect-based sentiment analysis[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Stroudsburg, PA: ACL, 2019: 504-515. [32] Liu N, Shen B. Aspect-based sentiment analysis with gated alternate neural network[J]. Knowledge-Based Systems, 2020, 188(105010. [33] Gan C, Wang L, Zhang Z, et al. Sparse attention based separable dilated convolutional neural network for targeted sentiment analysis[J]. Knowledge-Based Systems, 2020, 188: 104827. [34] Jiang B, Hou J, Zhou W, et al. METNet: A mutual enhanced transformation network for aspect-based sentiment analysis[C]//Proceedings of the 28th International Conference on Computational Linguistics, Stroudsburg, PA: ACL, 2020: 162-172. [35] Tang D, Qin B, Feng X, et al. Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, Stroudsburg, PA: ACL, 2016: 3298-3307. [36] Devlin J, Chang M-W, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, PA: ACL, 2019: 4171-4186. [37] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the International Conference on Neural Information Processing Systems, 2017, 30: 5998-6008. [38] Kingma D P, Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv: 14126980, 2014, [39] Wang Y, Huang M, Zhu X, et al. Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, Stroudsburg, PA: ACL, 2016: 606-615. [40] Chen P, Sun Z, Bing L, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, Stroudsburg, PA: ACL, 2017: 452-461. [41] 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. [42] Xu H, Liu B, Shu L, et al. BERT Post-Training for review reading comprehension and aspect-based sentiment analysis[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, PA: ACL, 2019: 2324-2335.
2022, Vol. 36 
