Abstract
The task of aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. Given an aspect and a sentence, the task classifies the sentiment polarity expressed on the target in the sentence. Previous work usually distinguish the sentiment based on one-way LSTM, which are often complicated and need more training time. In this paper, motivated by the BERT from Google AI Language, we propose a novel two-way encoder-decoder framework that automatically extracts appropriate sentiment information according to sequence to sequence reinforced learning. We use reinforcement learning to explore the space of possible extractive targets, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, Y., Xu, J., Yang, P., Sun, X.: Learning sentiment memories for sentiment modification without parallel data. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1097–1102 (2018)
Yi, L., Li, D., Li, P., Shi, S., Lam, W., Zhang, T.: Learning sentiment memories for sentiment modification without parallel data. In Empirical Methods in Natural Language Processing (EMNLP), pp. 3855–3864 (2018)
Keneshloo, Y., Shi, T., Ramakrishnan, N., Reddy, C.K.: Deep Reinforcement Learning for Sequence-to-Sequence Models. IEEE (2018)
Dahou, A., Elaziz, M.E.A., Zhou, J., Xiong, S.: Arabic sentiment classification using convolutional neural network and differential evolution algorithm. Comput. Int. Neurosc. 2537689:1–2537689:16 (2019)
Ma, R., Wang, K., Qiu, T., Sangaiah, A.K., Lin, D., Liaqat, H.B.: Feature-based compositing memory networks for aspect-based sentiment classification in social Internet of Things. Future Gener. Comput. Syst. 92, 879–888 (2019)
Zhang, T., Wu, X., Lin, M., Han, J., Hu, S.: Imbalanced sentiment classification enhanced with discourse marker. CoRR abs/1903.11919
Sharma, R., Bhattacharyya, P., Dandapat, S., Bhatty, H.S.: Identifying transferable information across domains for cross-domain sentiment classification. In: ACL, pp. 968–978 (2018)
Zhu, P., Qian, T.: Enhanced aspect level sentiment classification with auxiliary memory. In: COLING, pp. 1077–1087 (2018)
Lv, G., Wang, S., Liu, B., Chen, E., Zhang, K.: Sentiment classification by leveraging the shared knowledge from a sequence of domains. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11446, pp. 795–811. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18576-3_47
Liu, S., Lee, I.: Sentiment classification with medical word embeddings and sequence representation for drug reviews. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds.) HIS 2018. LNCS, vol. 11148, pp. 75–86. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01078-2_7
Iqbal, F., et al.: A hybrid framework for sentiment analysis using genetic algorithm based feature reduction. IEEE Access 7, 14637–14652 (2019)
Xu, G., Meng, Y., Qiu, X., Yu, Z., Wu, X.: Sentiment analysis of comment texts based on BiLSTM. IEEE Access 7, 51522–51532 (2019)
Seifollahi, S., Shajari, M.: Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction. J. Intell. Inf. Syst. 52(1), 57–83 (2019)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs: sentiment classification using machine learning techniques. In: Empirical Methods in Natural Language Processing, pp. 79–86 (2002)
Polanyi, L., Zaenen, A.: Contextual valence shifters. In: Shanahan, J.G., Qu, Y., Wiebe, J. (eds.) Computing Attitude and Affect in Text: Theory and Applications, pp. 1–10. Springer, Dordrecht (2006). https://doi.org/10.1007/1-4020-4102-0_1
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task4: aspect based sentiment analysis. In: ProWorkshop on Semantic Evaluation (SemEval-2014). Association for Computational Linguistics (2014)
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Empirical Methods in Natural Language Processing, pp. 1631–1642 (2016)
Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: EMNLP, pp. 463–472 (2017)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS (2014)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, pp. 10–21 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chu, H., Wu, Y., Tang, Y., Mao, C. (2019). Aspect Sentiment Classification Based on Sequence to Sequence Reinforced Learning. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-37429-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37428-0
Online ISBN: 978-3-030-37429-7
eBook Packages: Computer ScienceComputer Science (R0)