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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-030-01012-6_14
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