Abstract
Different aspects of a sentence may contain different sentiments, and sentiment descriptors for a given aspect exist in different places in the sentence, making it difficult to determine its sentiment polarity. Aiming at the above problems, a neighborhood network (Nenet) for aspect-based sentiment analysis is proposed. Firstly, the context information of the text is encoded, and the neighborhood information of the target aspect at the grammar level is extracted by using a graph convolutional neural network. At the same time, the convolutional neural network is used to extract the neighborhood information at the physical level, and the two extracted features are combined to improve the text expression ability. Finally, an attention mechanism is used to express the key information in sentences for judging sentiment polarity, and the final text representation is input to the sentiment analysis layer to predict sentiment polarity. Experiments are carried out on three standard datasets, and the experimental results show that the neighborhood network outperforms other baseline models in both index accuracy and F1 value.
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References
Hu, Z., Hu, J., Ding, W., Zheng, X.: Review sentiment analysis based on deep learning. In: 2015 IEEE 12th International Conference on e-Business Engineering, pp. 87–94 (2015)
Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167 (2012)
Giachanou, A., Crestani, F.: Like it or not: a survey of twitter sentiment analysis methods. ACM Comput. Surv. (CSUR) 49(2), 1–41 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Vo, D., Zhang, Y.: Target-dependent twitter sentiment classification with rich automatic features. AAAI Press (2015)
Chen, L., Yang, Y.: Emotional speaker recognition based on i-vector through Atom Aligned Sparse Representation. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7760–7764. IEEE (2013)
Han, H., Zhang, J., Yang, J., Shen, Y., Zhang, Y.: Generate domain-specific sentiment lexicon for review sentiment analysis. Multimedia Tools Appl. 77(16), 21265–21280 (2018). https://doi.org/10.1007/s11042-017-5529-5
Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., Khudanpur, S.: Recurrent neural network-based language model. In: Interspeech, pp. 1045–1048 (2010)
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016)
Ruder, S., Ghaffari, P., Breslin, J.G.: A hierarchical model of reviews for aspect-based sentiment analysis. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 999–1005 (2016)
Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. In: IJCAI 2017 Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 4068–4074 (2017)
Tay, Y., Tuan, L.A., Hui, S.C.: Dyadic memory networks for aspect-based sentiment analysis. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 107–116 (2017)
Lin, Y., Wang, C., Song, H., et al.: Multi-head self-attention transformation networks for aspect-based sentiment analysis. IEEE Access 9, 8762–8770 (2021)
Huang, B., Carley, K.: Parameterized convolutional neural networks for aspect level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1091–1096. Association for Computational Linguistics, Brussels (2018)
Fan, C., Gao, Q., Du, J., et al.: Convolution-based memory network for aspect-based sentiment analysis. In: The 41st International ACM SIGIR Conference on Research & Development In Information Retrieval, pp. 1161–1164. ACM, New York (2018)
Yan, C., Leibo, Y., Guanghe, Z., et al.: Text sentiment orientation analysis of multi-channels CNN and BIGRU based on attention mechanism. J. Comput. Res. Dev. 57(12), 2583–2595 (2020)
Pontiki, M., Galanis, D., Pavlopoulos, J., et al.: SemEval-2014 Task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014)
Dong, L., Wei, F., Tan, C., et al.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 49–54 (2014)
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615(2016)
Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 214–224 (2016)
Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 946–956 (2018)
Chen, Z., Qian, T.: Transfer capsule network for aspect level sentiment classification. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 547–556 (2019)
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Liu, H., Dou, Q. (2022). Neighborhood Network for Aspect-Based Sentiment Analysis. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_18
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DOI: https://doi.org/10.1007/978-3-031-03948-5_18
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