Abstract
Automatic Irony Detection refers to making computer understand the real intentions of human behind the ironic language. Much work has been done using classic machine learning techniques applied on various features. In contrast to sophisticated feature engineering, this paper investigates how the deep learning can be applied to the intended task with the help of word embedding. Three different deep learning models, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Attentive RNN, are explored. It shows that the Attentive RNN achieves the state-of-the-art on Twitter datasets. Furthermore, with a closer look at the attention vectors generated by Attentive RNN, an insight into how the attention mechanism helps find out the linguistic clues of ironic utterances is provided.
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
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of International Conference on Learning Representations (2015)
Barbieri, F., Saggion, H.: Modelling irony in Twitter. In: Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp. 56–64 (2014)
Ghosh, D., Guo, W., Muresan, S.: Sarcastic or not: word embeddings to predict the literal or sarcastic meaning of words. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1003–1012 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1746–1751 (2014)
Kreuz, R., Glucksberg, S.: How to be sarcastic: the echoic reminder theory of verbal irony. J. Exp. Psychol. Gen. 118(4), 374–386 (1989)
LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Mller, U., Sckinger, E., Simard, P., Vapnik, V.: Comparison of learning algorithms for handwritten digit recognition. In: Proceedings of International Conference on Artificial Neural Networks, pp. 53–60 (1995)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Reyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in Twitter. Lang. Resour. Eval. 47(1), 239–268 (2013)
Tang, Y.-j., Chen, H.-H.: Chinese irony corpus construction and ironic structure analysis. In: Proceedings of the 25th International Conference on Computational Linguistics, pp. 1269–1278 (2014)
Tieleman, T., Hinton, G.: Lecture 6.5 - rmsprop. COURSERA: Neural Networks for Machine Learning, pp. 26–31 (2012)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Acknowledgements
This research was partially supported by Ministry of Science and Technology, Taiwan, under grants MOST-104-2221-E-002-061-MY3 and MOST-105-2221-E-002-154-MY3.
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
Huang, YH., Huang, HH., Chen, HH. (2017). Irony Detection with Attentive Recurrent Neural Networks. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-56608-5_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56607-8
Online ISBN: 978-3-319-56608-5
eBook Packages: Computer ScienceComputer Science (R0)