[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] ZHONG Q, DING L, LIU J, et al. Knowledge graph augmented network towards multiview representation learning for aspect-based sentiment analysis[J]. arXiv:2201.04831, 2022.
[3] ZHOU J, ZHAO J, HUANG J X, et al. MASAD: a large-scale dataset for multimodal aspect-based sentiment analysis[J]. Neurocomputing, 2021, 455: 47-58.
[4] ZHOU J, HUANG J X, CHEN Q, et al. Deep learning for aspect-level sentiment classification: survey, vision, and challenges[J]. IEEE Access, 2019, 7: 78454-78483.
[5] TANG D, QIN B, FENG X, et al. Effective LSTMs for target-dependent sentiment classification[J]. arXiv:1512.01100, 2015.
[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] MA D, LI S, ZHANG X, et al. Interactive attention networks for aspect-level sentiment classification[J]. arXiv:1709.00893,2017.
[8] ZHANG C, LI Q, SONG D. Aspect-based sentiment classification with aspect-specific graph convolutional networks[J]. arXiv:1909.03477, 2019.
[9] PANG S, XUE Y, YAN Z, et al. Dynamic and multi-channel graph convolutional networks for aspect-based sentiment analysis[C]//Findings of the Association for Computational Linguistics (ACL-IJCNLP 2021), 2021: 2627-2636.
[10] CHEN G, TIAN Y, SONG Y. Joint aspect extraction and sentiment analysis with directional graph convolutional networks[C]//Proceedings of the 28th International Conference on Computational Linguistics, 2020: 272-279.
[11] LING Y, XIA R. Vision-language pre-training for multimodal aspect-based sentiment analysis[J]. arXiv:2204.07955, 2022.
[12] YANG L, NA J C, YU J. Cross-modal multitask transformer for end-to-end multimodal aspect-based sentiment analysis[J]. Information Processing & Management, 2022, 59(5): 103038.
[13] XU N, MAO W, CHEN G. Multi-interactive memory network for aspect based multimodal sentiment analysis[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019: 371-378.
[14] YU J, JIANG J, XIA R. Entity-sensitive attention and fusion network for entity-level multimodal sentiment classification[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, 28: 429-439.
[15] WU H, CHENG S, WANG J, et al. Multimodal aspect extraction with region-aware alignment network[C]//CCF International Conference on Natural Language Processing and Chinese Computing. Cham: Springer, 2020: 145-156.
[16] JU X, ZHANG D, XIAO R, et al. Joint multi-modal aspect-sentiment analysis with auxiliary cross-modal relation detection[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021: 4395-4405.
[17] HE R, LEE W S, NG H T, et al. Exploiting document knowledge for aspect-level sentiment classification[J]. arXiv:1806.
04346, 2018.
[18] KINGMA DIEDERIK P, ADAM J B. A method for stochastic optimization[J]. arXiv:1412.6980, 2014.
[19] TANG D, QIN B, LIU T. Aspect level sentiment classification with deep memory network[J]. arXiv:1605.08900, 2016.
[20] LIU F, COHN T, BALDWIN T. Recurrent entity networks with delayed memory update for targeted aspect-based sentiment analysis[J]. arXiv:1804.11019, 2018.
[21] YU J, JIANG J. Adapting BERT for target-oriented multimodal sentiment classification[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019: 5408-5414.
[22] WANG J, GU D, YANG C, et al. Targeted aspect based multimodal sentiment analysis: an attention capsule extraction and multi-head fusion network[J]. arXiv:2103.07659, 2021. |