Abstract
Accurate estimation of emotions in SNS posts plays an essential role in a wide variety of real world applications such as intelligent dialogue systems, review analysis for recommendations and so on. In this paper, we focus on developing accurate models for estimating types of emotions and their intensities in Japanese tweets by using multi-task deep learning. More concretely, three deep learning models for estimating intensities of emotions were extended to be able to predict the emotional types and their intensities at a time. The effectiveness of the developed models was confirmed through experiments using the database of Japanese tweets annotated with intensity scores of four types of emotions using best-worst scaling.
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
References
Mohammad, S.M., Bravo-Marquez, F.: WASSA-2017 shared task on emotion intensity. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (2017)
Mohammad, S.M., Bravo-Marquez, F., Salameh, M., Kiritchenko, S.: SemEval-2018 task 1: affect in Tweets. In: Proceedings of the 12th International Workshop on Semantic Evaluation (2018)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. In: Final Projects from CS224N for Spring 2008/2009 at The Stanford Natural Language Processing Group (2009)
Mohammad, S.M.: Word affect intensities. arXiv preprint arXiv:1704.08798 (2017)
Keras Documentation: Multi-input and multi-output models. https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models
Himeno, S., Aono, M.: Estimation of Twitter emotion intensity by using transfer learning of feature tensors and emotion lexicon. IEICE Technical report DE2018-11 (2018). (in Japanese)
Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., Lehmann, S.: Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1615–1625 (2017)
Duppada, V., Hiray, S.: Seernet at EmoInt-2017: tweet emotion intensity estimator. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 205–211 (2017)
Goel, P., Kulshreshtha, D., Jain, P., Shukla, K.K.: Prayas at EmoInt 2017: an ensemble of deep neural architectures for emotion intensity prediction in tweets. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 58–65 (2017)
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)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Sig. Process. 45, 2673–2681 (1997)
He, Y., Yu, L.-C., Lai, K.R., Liu, W.: YZU-NLP at EmoInt-2017: determining emotion intensity using a bi-directional LSTM-CNN model. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 238–242 (2017)
Zhang, Y., Yuan, H., Wang, J., Zhang, X.: YNU-HPCC at EmoInt-2017: using a CNN-LSTM model for sentiment intensity prediction. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 200–204 (2017)
Duppada, V., Jain, R., Hiray, S.: SeerNet at SemEval-2018 task 1: domain adaptation for affect in tweets. In: Proceedings of the 12th International Workshop on Semantic Evaluation, pp. 18–23 (2018)
Baziotis, C., Nikolaos, A., Chronopoulou, A., Kolovou, A., Paraskevopoulos, G., Ellinas, N., Narayanan, S., Potamianos, A.: NTUA-SLP at SemEval-2018 task 1: predicting affective content in tweets with deep attentive RNNs and transfer learning. In: Proceedings of the 12th International Workshop on Semantic Evaluation, pp. 245–255 (2018)
Park, J.H., Xu, P., Fung, P.: PlusEmo2Vec at SemEval-2018 task 1: exploiting emotion knowledge from emoji and #hashtags. In: Proceedings of the 12th International Workshop on Semantic Evaluation, pp. 264–272 (2018)
Liang, D., Xu, W., Zhao, Y.: Combining word-level and character-level representations for relation classification of informal text. In: Proceedings of the 2nd Workshop on Representation Learning for NLP, pp. 43–47 (2017)
Cho, K., Merrienboer, B., Gulcehre, C., 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)
Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, vol. 2, pp. 2377–2385 (2015)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint, arXiv:1412.6980 (2014)
Acknowledgements
This work was partially supported by JSPS KAKENHI Grant Number 17K00315.
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
Sato, K., Ozaki, T. (2019). Estimation of Emotion Type and Intensity in Japanese Tweets Using Multi-task Deep Learning. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-15035-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-15035-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15034-1
Online ISBN: 978-3-030-15035-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)