Abstract
As a fine-grained classification task, aspect-level sentiment classification aims at determining the sentiment polarity given a particular target in a sentence. The key point of this task is to distinguish target-related words and target-unrelated words. To this end, attention mechanism is introduced into this task, which assigns high attention weights to target-related words and ignores target-unrelated words according to the semantic relationships between context words and target. However, existing work not explicitly take into account the position information of context words when calculating the attention weights. Actually, position information is very important for detecting the relevance of the word to target, where words that are closer to the target usually make a greater contribution for determining the sentiment polarity. In this work, we propose a novel approach to combine position information and attention mechanism. We get the position distribution according to the distances between context words and target, then leverage the position distribution to modify the attention weight distribution. In addition, considering that sentiment polarity is usually represented by a phrase, we use CNN for sentiment classification which can capture local n-gram features. We test our model on two public benchmark datasets from SemEval 2014, and the experimental results demonstrate the effectiveness of our approach.
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Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014). https://doi.org/10.3115/v1/D14-1181
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012). https://doi.org/10.2200/S00416ED1V01Y201204HLT016
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 151–160. Association for Computational Linguistics (2011)
Wang, Y., Huang, M., Zhao, L., et al.: 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). https://doi.org/10.18653/v1/D16-1058
Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893 (2017). https://doi.org/10.24963/ijcai.2017/568
He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: Effective attention modeling for aspect-level sentiment classification. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 1121–1131 (2018)
Gu, S., Zhang, L., Hou, Y., Song, Y.: A position-aware bidirectional attention network for aspect-level sentiment analysis. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 774–784 (2018)
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: Semeval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014). https://doi.org/10.3115/v1/S14-2004
Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 56–65. Association for Computational Linguistics (2010)
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. arXiv preprint arXiv:1512.01100 (2015)
Wang, B., Lu, W.: Learning latent opinions for aspect-level sentiment classification. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Liu, J., Zhang, Y.: Attention modeling for targeted sentiment. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, vol. 2, pp. 572–577 (2017). https://doi.org/10.18653/v1/E17-2091
Zhang, Y., Zhong, V., Chen, D., Angeli, G., Manning, C.D.: Position-aware attention and supervised data improve slot filling. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017). https://doi.org/10.18653/v1/D17-1004
Weston, J., Chopra, S., Bordes, A.: Memory networks. arXiv preprint arXiv:1410.3916 (2014)
Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900 (2016). https://doi.org/10.18653/v1/D16-1021
Yang, J., Yang, R., Wang, C., Xie, J.: Multi-entity aspect-based sentiment analysis with context, entity and aspect memory. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018b). https://doi.org/10.1145/3321125
Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015). https://doi.org/10.18653/v1/D15-1166
Yang, B., Tu, Z., Wong, D.F., Meng, F., Chao, L.S., Zhang, T.: Modeling localness for self-attention networks. arXiv preprint arXiv:1810.10182 (2018a). https://doi.org/10.18653/v1/D18-1475
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). https://doi.org/10.3115/v1/D14-1162
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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Wang, D., Liu, T., Wang, B. (2019). Revising Attention with Position for Aspect-Level Sentiment Classification. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_12
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