Abstract
In Natural Language Processing, stance detection is the computational task of deciding whether a piece of text expresses a favourable or unfavourable attitude (or stance) towards a given topic. Stance detection may be divided into two subtasks: deciding whether a piece of text conveys any stance towards the target topic and, once we have established that the text does convey a stance, determining its polarity (e.g., favourable or unfavourable) towards the target. Both tasks - hereby called stance recognition and (stance) polarity classification - are the focus of the present work. Taking as a basis a corpus of 13.7k tweets in the Brazilian Portuguese language, and which conveys stances towards five moral issues (abortion legislation, death penalty, drug legalisation, lowering of criminal age, and racial quotas at universities), we compare a number of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) models for stance recognition and polarity classification. In doing so, the two tasks are addressed both independently and as a joint model. Results suggest that the use of BiLSTM models with attention mechanism outperform the alternatives under consideration, and pave the way for more comprehensive studies in this domain.
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
Notes
- 1.
Additional teams also participated in the unsupervised track of the competition, which is presently not discussed.
- 2.
For instance, ‘death penalty for people who litter the streets’ does not convey a genuine stance towards the issue of death penalty.
References
Anand, P., Walker, M., Abbott, R., Tree, J.E.F., Bowmani, R., Minor, M.: Cats rule and dogs drool!: classifying stance in online debate. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (ACL-HLT 2011), pp. 1–9. Association for Computational Linguistics, Portland (2011)
Augenstein, I., Rocktaschel, T., Vlachos, A., Bontcheva, K.: Stance detection with bidirectional conditional encoding. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 876–885. Association for Computational Linguistics, Austin (2016)
Custódio, J.E., Paraboni, I.: EACH-USP ensemble cross-domain authorship attribution. In: Working Notes Papers of the Conference and Labs of the Evaluation Forum (CLEF-2018), Avignon, France, vol. 2125 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171–4186. Association for Computational Linguistics, Minneapolis (2019). https://doi.org/10.18653/v1/N19-1423
Ghosh, S., Singhania, P., Singh, S., Rudra, K., Ghosh, S.: Stance detection in web and social media: a comparative study. In: Crestani, F., et al. (eds.) CLEF 2019. LNCS, vol. 11696, pp. 75–87. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28577-7_4
Hasan, K.S., Ng, V.: Stance classification of ideological debates: data, models, features, and constraints. In: Proceedings of the International Joint Conference on Natural Language Processing, pp. 1348–1356. Association for Computational Linguistics, Nagoya (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hsieh, F.C., Dias, R.F.S., Paraboni, I.: Author profiling from facebook corpora. In: 11th International Conference on Language Resources and Evaluation (LREC-2018), ELRA, Miyazaki, Japan, pp. 2566–2570 (2018)
Lai, M., Hernández Farías, D.I., Patti, V., Rosso, P.: Friends and enemies of Clinton and Trump: using context for detecting stance in political tweets. In: Sidorov, G., Herrera-Alcántara, O. (eds.) MICAI 2016. LNCS (LNAI), vol. 10061, pp. 155–168. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62434-1_13
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119. Curran Associates, Inc. (2013)
Mohammad, S.M., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: A dataset for detecting stance in tweets. In: Proceedings of 10th Edition of the the Language Resources and Evaluation Conference (LREC-2016), Portoroz, Slovenia (2016)
Mohammad, S.M., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of the International Workshop on Semantic Evaluation, San Diego, California, USA (2016)
Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Trans. Internet Technol. Argum. Soc. Media 17(3), 23 (2017)
Rantanen, A., Salminen, J., Ginter, F., Jansen, J.: Classifying online corporate reputation with machine learning: a study in the banking domain. Internet Res., November 2019. https://doi.org/10.1108/INTR-07-2018-0318
dos Santos, W.R., Paraboni, I.: Moral stance recognition and polarity classification from twitter and elicited text. In: Recents Advances in Natural Language Processing (RANLP-2019), Varna, Bulgaria, pp. 1069–1075 (2019). https://doi.org/10.26615/978-954-452-056-4_123
Silva, B.B.C., Paraboni, I.: Learning personality traits from Facebook text. IEEE Lat. Am. Trans. 16(4), 1256–1262 (2018). https://doi.org/10.1109/TLA.2018.8362165
Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 5998–6008. Curran Associates, Inc. (2017)
Wei, W., Zhang, X., Liu, X., Chen, W., Wang, T.: pkudblab at SemEval-2016 task 6: a specific convolutional neural network system for effective stance detection. In: Proceedings of the International Workshop on Semantic Evaluation, San Diego, California, USA (2016)
Zarrella, G., Marsh, A.: MITRE at SemEval-2016 task 6: transfer learning for stance detection. In: Proceedings of the International Workshop on Semantic Evaluation, San Diego, California, USA (2016)
Acknowledgements
This work has been supported by the University of São Paulo PRP grant nr. 668/2018.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Pavan, M.C., dos Santos, W.R., Paraboni, I. (2020). Twitter Moral Stance Classification Using Long Short-Term Memory Networks. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-61377-8_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61376-1
Online ISBN: 978-3-030-61377-8
eBook Packages: Computer ScienceComputer Science (R0)