Imbalanced Sentiment Classification Enhanced with Discourse Marker

Preprint English OPEN
Zhang, Tao; Wu, Xing; Lin, Meng; Han, Jizhong; Hu, Songlin;
  • Subject: Computer Science - Computation and Language | Computer Science - Machine Learning

Imbalanced data commonly exists in real world, espacially in sentiment-related corpus, making it difficult to train a classifier to distinguish latent sentiment in text data. We observe that humans often express transitional emotion between two adjacent discourses with ... View more
  • References (23)
    23 references, page 1 of 3

    1. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority oversampling technique. Journal of artificial intelligence research 16, 321-357 (2002)

    2. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018), http://arxiv. org/abs/1810.04805

    3. Dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. pp. 69-78 (2014)

    4. Fadaee, M., Bisazza, A., Monz, C.: Data augmentation for low-resource neural machine translation. arXiv preprint arXiv:1705.00440 (2017)

    5. He, R., McAuley, J.: Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: proceedings of the 25th international conference on world wide web. pp. 507-517. International World Wide Web Conferences Steering Committee (2016)

    6. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735- 1780 (1997)

    7. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 168- 177. ACM (2004)

    8. Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., Xing, E.P.: Toward controlled generation of text. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 1587-1596. JMLR. org (2017)

    9. Jia, R., Liang, P.: Adversarial examples for evaluating reading comprehension systems. arXiv preprint arXiv:1707.07328 (2017)

    10. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  • Related Research Results (1)
  • Related Organizations (3)
  • Metrics
Share - Bookmark