[1] HU M Q, LIU B. Mining and summarizing customer reviews[C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004: 168-177.
[2] SUN K, ZHANG R, MENSAH S, et al. Aspect-level sentiment analysis via convolution over dependency tree[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 5679-5688.
[3] HUANG B, CARLEY K M. Syntax-aware aspect level sentiment classification with graph attention networks[J]. arXiv:1909.02606, 2019.
[4] JIANG L, YU M, ZHOU M, et al. Target-dependent twitter sentiment classification[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011: 151-160.
[5] WANG K, SHEN W, YANG Y, et al. Relational graph attention network foraspect-based sentiment analysis[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 3229-3238.
[6] 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.
[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] WANG J, LI J, LI S, et al. Aspect sentiment classification with both word-level and clause-level attention networks[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018: 4439-4445.
[9] ZHANG M, QIAN T. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020: 3540-3549.
[10] REN Z, ZENG G, LIU C, et al. A lexicon-enhanced attention network for aspect-level sentiment analysis[J]. IEEE Access, 2020, 8: 93464-93471.
[11] DISTANTE D, FARALLI S, RITTINGHAUS S, et al. DomainSenticNet: an ontology and a methodology enabling domain-aware sentic computing[J]. Cognitive Computation, 2022, 14: 62-77.
[12] XING F Z, PALLUCCHINI F, CAMBRIA E. Cognitive-inspired domain adaptation of sentiment lexicons[J]. Information Processing and Management, 2019, 56(3): 554-564.
[13] DEVLIN J, CHANG M, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 4171-4186.
[14] ZHANG C, LI Q, SONG D. Aspect-based sentiment classification with aspect-specific graphConvolutional networks[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 4567-4577.
[15] FAN F, FENG Y, ZHAO D. Multi-grained attention network for aspect-level sentiment classification[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018: 3433-3442.
[16] LI D, 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, 2014: 49-54.
[17] 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.
[18] 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, 2016: 3298-3307.
[19] MA D H, LI S J, ZHANG X D, et al. Interactive attention networks for aspect-level sentiment classification[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017: 4068-4074.
[20] CHEN C, TENG Z, ZHANG Y. Inducing target-specific latent structures or aspect sentiment classification[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020: 5596-5607.
[21] 王汝言, 陶中原, 赵荣剑, 等. 多交互图卷积网络用于方面情感分析[J]. 电子与信息学报, 2022, 44(3): 1111-1118.
WANG R Y, TAO Z Y, ZHAO R J, et al. Multi interaction graph convolutional networks for aspect sentiment analysis[J]. Journal of Electronics and Information Technology, 2022, 44 (3): 1111-1118.
[22] 杨先凤, 汤依磊, 李自强. 基于交替注意力机制和图卷积网络的方面级情感分析模型[J]. 计算机应用, 2024, 44(4): 1058-1064.
YANG X F, TANG Y L, LI Z Q. Aspect level sentiment analysis model based on alternating attention mechanism and graph convolutional network[J]. Computer Applications, 2024, 44(4): 1058-1064.
[23] ZHOU J, HUANG, HU Q V, et al. SK-GCN: modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification[J]. Knowledge-Based Systems, 2020, 205(3): 214-223. |