Abstract
Sentiment classification is a critical task in sentiment analysis and other text mining applications. As a sub-problem of sentiment classification, positive and unlabeled learning or positive-unlabeled learning (PU learning) problem widely exists in real-world cases, but it has not been given enough attention. In this paper, we aim to solve PU learning problem under the framework of adversarial training and neural network. We propose a novel model for PU learning problem, which is based on adversarial training and attention-based long short-term memory (LSTM) network. In our model, we design a new adversarial training technique. We conducted extensive experiments on two real-world datasets. The experimental results demonstrate that our proposed model outperforms the compared methods, including the well-known traditional methods and state-of-the-art methods. We also report the training time, and discuss the sensitivity of our model to parameters.
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References
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems (2015)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)
Dey, L., Chakraborty, S., Biswas, A., Bose, B., Tiwari, S.: Sentiment analysis of review datasets using naïve bayes and k-nn classifier. Int. J. Inf. Eng. Electron. Bus. 4, 54–62 (2016)
Du Plessis, M.C., Niu, G., Sugiyama, M.: Analysis of learning from positive and unlabeled data. In: Advances in Neural Information Processing Systems (NIPS), pp. 703–711 (2014)
Gao, H., Huang, W., Yang, X., Duan, Y., Yin, Y.: Toward service selection for workflow reconfiguration: an interface-based computing solution. Future Gener. Comput. Syst. 87, 298–311 (2018)
Gao, H., Mao, S., Huang, W., Yang, X.: Applying probabilistic model checking to financial production risk evaluation and control: a case study of Alibaba’s Yu’e Bao. IEEE Trans. Comput. Soc. Syst. 5(3), 785–795 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NIPS), pp. 2672–2680 (2014)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Proceedings of the International Conference on Learning Representations (ICLR), pp. 1–11 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Johnson, R., Zhang, T.: Semi-supervised convolutional neural networks for text categorization via region embedding. In: Advances in Neural Information Processing Systems (NIPS), pp. 919–927 (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)
Kurakin, A., Boneh, D., Tramèr, F., Goodfellow, I., Papernot, N., McDaniel, P.: Ensemble adversarial training: attacks and defenses. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)
Li, H., Liu, B., Mukherjee, A., Shao, J.: Spotting fake reviews using positive-unlabeled learning. Computacióny Sistemas 18(3), 467–475 (2014)
Li, X.-L., Liu, B.: Learning from positive and unlabeled examples with different data distributions. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 218–229. Springer, Heidelberg (2005). https://doi.org/10.1007/11564096_24
Lin, J., Mao, W., Zeng, D.D.: Personality-based refinement for sentiment classification in microblog. Knowl.-Based Syst. 132, 204–214 (2017)
Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge (2015)
Liu, B., Dai, Y., Li, X., Lee, W.S., Yu, P.S.: Building text classifiers using positive and unlabeled examples. In: Third IEEE International Conference on Data Mining (ICDM), pp. 179–186 (2003)
Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 142–150 (2011)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)
Miyato, T., Dai, A.M., Goodfellow, I.: Adversarial training methods for semi-supervised text classification. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2007)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372–387 (2016)
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Ren, Y., Ji, D., Zhang, H.: Positive unlabeled learning for deceptive reviews detection. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 488–498 (2014)
Wang, Y., Huang, M., Zhao, L., Zhu, X.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of International Conference on Empirical Methods in Natural Language Processing (EMNLP) (2016)
Wu, Y., Bamman, D., Russell, S.: Adversarial training for relation extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1778–1783 (2017)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of NAACL, pp. 1480–1489 (2016)
Yuan, Z., Wu, S., Wu, F., Liu, J., Huang, Y.: Domain attention model for multi-domain sentiment classification. Knowl.-Based Syst. 155, 1–10 (2018)
Acknowledgements
This paper is granted by Fundamental Research Fund for Central Universities (No. JBX171007), National Natural Science Fund of China (No. 61702391), Natural Science Foundation of Shaanxi province and Zhejiang province (No. 2018JQ6050, No. LY12F02003). Meanwhile, the authors would like to thank Minhao Ni for her valuable suggestions in experiment design.
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Xu, Y. et al. (2019). Positive-Unlabeled Learning for Sentiment Analysis with Adversarial Training. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_25
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