Abstract
Recently, stance detection on Twitter has been a hot research topic in the natural language processing community. Most previous studies have assumed that only one target is involved in a tweet and perform single-target stance detection. In this study, we address a more challenging version of this task, namely two-target stance detection, where two targets are involved. Specifically, we first define four categories of text zones related to two targets, i.e., Target 1, Target 2, Non-target, and Other, and propose an unsupervised approach to automatically obtain these zones. Then, we propose a hierarchical neural network to perform stance detection for each target where multiple LSTM layers are leveraged to encode the target-related zones and a Bidirectional LSTM layer to encode the outputs from the LSTM layers. Moreover, we introduce a target-related attention mechanism in the hierarchical network. Empirical studies demonstrate the effectiveness of the proposed approach to two-target stance detection.
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
Thomas, M., Pang, B., Lee, L.: Get out the vote: determining support or opposition from congressional floor-debate transcripts. In: Proceedings of EMNLP, pp. 327–335 (2006)
Yessenalina, A., Yue, Y., Cardie, C.: Multi-level structured models for document-level sentiment classification. In: Proceedings of EMNLP, pp. 1046–1056 (2010)
Agrawal, R., Rajagopalan, S., Srikant, R., Xu, Y.: Mining newsgroups using networks arising from social behavior. In: Proceedings of WWW, pp. 529–535 (2003)
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: The Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 1–9 (2011)
Somasundaran, S., Wiebe, J.: Recognizing stance in ideological online debates. In: The Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 116–124 (2010)
Mohammad, S.M., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: Semeval-2016 task 6: detecting stance in tweets. In: The Workshop on Semantic Evaluation (SemEval-2016), pp. 31–41 (2016)
Du, J., Xu, R., He, Y., Gui, L.: Stance classification with target-specific neural attention networks. In: Proceedings of IJCAI, pp. 3988–3994 (2017)
Sobhani, P., Mohammad, S.M., Kiritchenko, S.: Detecting stance in tweets and analyzing its interaction with sentiment. In: Joint Conference on Lexical and Computational Semantics, pp. 159–169 (2016)
Sobhani, P., Inkpen, D., Zhu, X.: A dataset for multi-target stance detection. In: Proceedings of EACL, pp. 551–557 (2017)
Somasundaran, S., Wiebe, J.: Recognizing stance in online debates. In: Proceedings of ACL and AFNLP, pp. 116–124 (2009)
Hasan, K.S., Ng, V.: Stance classification of ideological debates: data, models, features, and constraints. In: Joint Conference on Natural Language Processing, pp. 1348–1356 (2013)
Faulkner, A.: Automated classification of stance in student essays: an approach using stance target information and the Wikipedia link-based measure. Science 376(12), 86 (2014)
Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of EMNLP, pp. 1589–1599 (2011)
Hasan, K.S., Ng, V.: Why are you taking this stance? Identifying and classifying reasons in ideological debates. In: Proceedings of EMNLP, pp. 751–762 (2014)
Burfoot, C., Bird, S., Baldwin, T.: Collective classification of congressional floor-debate transcripts. In: Proceedings of ACL, pp. 1506–1515 (2011)
Walker, M.A., Anand, P., Abbott, R., Grant, R.: Stance classification using dialogic properties of persuasion. In: Proceedings of ACL, pp. 592–596 (2012)
Ebrahimi, J., Dou, D., Lowd, D.: A joint sentiment-target-stance model for stance classification in tweets. In: Proceedings of CL, pp. 2656–2665 (2016)
Augensten, I., Rocktaschel, T., Vlachos, A., Bontcheva, K.: Stance detection with bidirectional conditional encoding. In: Proceedings of EMNLP, pp. 876–885 (2016)
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(11), 2673–2681 (1997)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of EMNLP, vol. 12, pp. 1532–1543 (2014)
Acknowledgments
This research work has been partially supported by two NSFC grants, No. 61672366 and No. 61331011.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, H., Li, S., Zhou, G. (2018). Two-Target Stance Detection with Target-Related Zone Modeling. In: Zhang, S., Liu, TY., Li, X., Guo, J., Li, C. (eds) Information Retrieval. CCIR 2018. Lecture Notes in Computer Science(), vol 11168. Springer, Cham. https://doi.org/10.1007/978-3-030-01012-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-01012-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01011-9
Online ISBN: 978-3-030-01012-6
eBook Packages: Computer ScienceComputer Science (R0)