Abstract
Stance detection aims to determine the stance of a text towards a given target. Different from aspect-level sentiment classification, the target may not appear in the text. While existing models have achieved great success in this task using deep neural networks, their performances still drop sharply on cases where targets are not directly mentioned in texts, even with ‘target-aware’ structures. We argue that the nonalignment between targets and potentially opinioned terms in texts causes such failure and this could be remedied with external knowledge as a bridge. To this end, we propose RelNet, which leverages multiple external knowledge bases as bridges to explicitly link potentially opinioned terms in texts to targets of interest. Experiments on the well-adopted SemEval 2016 task 6 dataset demonstrate the effectiveness of the proposed model, especially on the subset where targets do not appear in texts.
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
Abu-Jbara, A., Dasigi, P., Diab, M., Radev, D.: Subgroup detection in ideological discussions. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Vol. 1, pp. 399–409 (2012)
Augenstein, I., Rocktäschel, T., Vlachos, A., Bontcheva, K.: Stance detection with bidirectional conditional encoding. arXiv preprint arXiv:1606.05464 (2016)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dey, K., Shrivastava, R., Kaushik, S.: Topical stance detection for twitter: a two-phase LSTM model using attention. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 529–536. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76941-7_40
Du, J., Gui, L., Xu, R., Xia, Y., Wang, X.: Commonsense knowledge enhanced memory network for stance classification. IEEE Intell Syst 35(4), 102–109 (2020)
Du, J., Xu, R., He, Y., Gui, L.: Stance classification with target-specific neural attention networks. In: International Joint Conferences on Artificial Intelligence (2017)
Ferreira, W., Vlachos, A.: Emergent: a novel data-set for stance classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies, pp. 1163–1168 (2016)
Hanawa, K., Sasaki, A., Okazaki, N., Inui, K.: Stance detection attending external knowledge from wikipedia. J Inf Process 27, 499–506 (2019)
Hasan, K.S., Ng, V.: Stance classification of ideological debates: Data, models, features, and constraints. In: Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 1348–1356 (2013)
Kim, S.M., Hovy, E.: Crystal: analyzing predictive opinions on the web. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 1056–1064 (2007)
Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 31–41 (2016)
Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Transac Internet Technol 17(3), 1–23 (2017)
Murakami, A., Raymond, R.: Support or oppose? Classifying positions in online debates from reply activities and opinion expressions. In: Coling 2010, Posters, pp. 869–875 (2010)
Qiu, M., Yang, L., Jiang, J.: Modeling interaction features for debate side clustering. In: Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pp. 873–878 (2013)
Siddiqua, U.A., Chy, A.N., Aono, M.: Tweet stance detection using an attention based neural ensemble model. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol 1, pp. 1868–1873 (2019)
Somasundaran, S., Wiebe, J.: Recognizing stances in ideological on-line debates. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 116–124 (2010)
Sridhar, D., Foulds, J., Huang, B., Getoor, L., Walker, M.: Joint models of disagreement and stance in online debate. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Vol. 1, pp. 116–125 (2015)
Su, Z., Zhu, C., Dong, Y., Cai, D., Chen, Y., Li, J.: Learning visual knowledge memory networks for visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7736–7745 (2018)
Thomas, M., Pang, B., Lee, L.: Get out the vote: determining support or opposition from congressional floor-debate transcripts. arXiv:cs/0607062v3 (2006)
Wei, P., Mao, W., Zeng, D.: A target-guided neural memory model for stance detection in twitter. In: IJCNN, pp. 1–8. IEEE (2018)
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: SemEval-2016, pp. 384–388 (2016)
Young, T., Cambria, E., Chaturvedi, I., Zhou, H., Biswas, S., Huang, M.: Augmenting end-to-end dialogue systems with commonsense knowledge. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 32 (2018)
Yuan, J., Zhao, Y., Xu, J., Qin, B.: Exploring answer stance detection with recurrent conditional attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7426–7433 (2019)
Zarrella, G., Marsh, A.: Mitre at semeval-2016 task 6: transfer learning for stance detection. arXiv preprint arXiv:1606.03784 (2016)
Acknowledgements
This work was supported by the National Key R&D Program of China (Grant No. 2018YFB1005103), and the National Natural Science Foundation of China (Grant Nos. 61632011 and 61772153). We would particularly like to acknowledge Yanyue Lu, Yijian Tian, Hao Yang, and Yang Wu, for their kind help and useful discussion. Our deepest gratitude goes to the anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, X., Yuan, J., Zhao, Y., Qin, B. (2021). Knowledge Enhanced Target-Aware Stance Detection on Tweets. In: Qin, B., Jin, Z., Wang, H., Pan, J., Liu, Y., An, B. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction. CCKS 2021. Communications in Computer and Information Science, vol 1466. Springer, Singapore. https://doi.org/10.1007/978-981-16-6471-7_13
Download citation
DOI: https://doi.org/10.1007/978-981-16-6471-7_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6470-0
Online ISBN: 978-981-16-6471-7
eBook Packages: Computer ScienceComputer Science (R0)