Abstract
Advances in neural network models and deep learning mark great impact on sentiment analysis, where models based on recursive or convolutional neural networks show state-of-the-art results leaving behind non-neural models like SVM or traditional lexicon-based approaches. We present Tree-Structured Gated Recurrent Unit network, which exhibits greater simplicity in comparison to the current state of the art in sentiment analysis, Tree-Structured LSTM model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Wu, D., Carpuat, M., Carreras, X., Vecchi, E.M. (eds.) Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111 (2014)
Cho, K., van Merriënboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1724–1734 (2014)
Chung, J., Gülçehre, Ç., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs/1412.3555 (2014)
Goller, C., Küchler, A.: Learning task-dependent distributed representations by backpropagation through structure. In: Proceedings of the International Conference on Neural Networks, ICNN 1996, pp. 347–352 (1996)
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space Odyssey. arXiv preprint arxiv:1503.04069 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Irsoy, O., Cardie, C.: Deep recursive neural networks for compositionality in language. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2096–2104 (2014)
Józefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: Bach, F.R., Blei, D.M. (eds.) Proceedings of the 32nd International Conference on Machine Learning, pp. 2342–2350 (2015)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Toutanova, K., Wu, H. (eds.) Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 655–665 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1746–1751 (2014)
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)
dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Tsujii, J., Hajič, J. (eds.) Proceedings of COLING 2014, The 25th International Conference on Computational Linguistics, Technical Papers, pp. 69–78 (2014)
Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector spaces. In: Tsujii, J., Henderson, J., Paşca, M. (eds.) Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, pp. 1201–1211 (2012)
Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.D.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Barzilay, R., Johnson, M. (eds.) Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 151–161 (2011)
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)
Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: Zong, C., Strube, M. (eds.) Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 1556–1566 (2015)
Takala, P., Malo, P., Sinha, A., Ahlgren, O.: Gold-standard for topic-specific sentiment analysis of economic texts. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation, LREC, vol. 2014, pp. 2152–2157 (2014)
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Màrquez, L., Callison-Burch, C., Su, J. (eds.) Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432 (2015)
Zhu, X., Sobhani, P., Guo, H.: Long short-term memory over recursive structures. In: Bach, F.R., Blei, D.M. (eds.) Proceedings of the 32nd International Conference on Machine Learning, pp. 1604–1612 (2015)
Acknowledgments
This research was supported in part by PL-Grid Infrastructure. The research was also partially financed by AGH University of Science and Technology Statutory Fund.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kuta, M., Morawiec, M., Kitowski, J. (2017). Sentiment Analysis with Tree-Structured Gated Recurrent Units. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-64206-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64205-5
Online ISBN: 978-3-319-64206-2
eBook Packages: Computer ScienceComputer Science (R0)