Abstract
Classifying the stance expressed in text towards specific target, namely stance detection, is a challenging task. The biggest distinction between stance detection and ordinary sentiment classification is that the determination of the stance is dependent on target while the target might not be explicitly mentioned in text. This indicates that the stance detection is not only dependent on the text content but also highly determined by the concerned target. To this end, we propose a neural network based model for stance detection, which leverages target-oriented information by utilizing target-augmented embedding and attention mechanism. The attention mechanism here is expected to locate the important parts of a text. The evaluation on SemEval 2016 Task 6 Twitter Stance Detection dataset shows that our proposed model achieves the state-of-the-art results.
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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, pp. 1–9. Association for Computational Linguistics (2011)
Augenstein, I., Rocktäschel, 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 (2016)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate (2014). arXiv:1409.0473
Du, J., Gui, L., Xu, R.: Extracting opinion expression with neural attention. In: Li, Y., Xiang, G., Lin, H., Wang, M. (eds.) SMP 2016. CCIS, vol. 669, pp. 151–161. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-2993-6_13
Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)
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)
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. ACL (2016)
Gui, L., Xu, R., He, Y., Lu, Q., Wei, Z.: Intersubjectivity and sentiment: from language to knowledge. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 2789–2795 (2016)
Hasan, K.S., Ng, V.: Stance classification of ideological debates: data, models, features, and constraints. In: International Joint Conference on Natural Language Processing, pp. 1348–1356 (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Liu, B.: Sentiment analysis and opinion mining. In: Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167 (2012)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv:1301.3781
Mohammad, S.M., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of SemEval, vol. 16 (2016)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. pp. 1556–1566. Association of Computational Linguistics (2015)
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432 (2015)
Vijayaraghavan, P., Sysoev, I., Vosoughi, S., Roy, D.: Deepstance at semeval-2016 task 6: detecting stance in tweets using character and word-level CNNs (2016). arXiv:1606.05694
Walker, M.A., Anand, P., Abbott, R., Grant, R.: Stance classification using dialogic properties of persuasion. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 592–596. Association for Computational Linguistics (2012)
Wang, Y., Huang, M., Zhao*, L., Zhu, X.: Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615. Association of Computational Linguistics (2016)
Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the 32nd International Conference on Machine, pp. 77–81 (2015)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2016)
Acknowledgments
This work was supported by the National Natural Science Foundation of China 61370165, U1636103, 61632011, National 863 Program of China 2015AA015405, Shenzhen Foundational Research Funding JCYJ20150625142543470 and Guangdong Provincial Engineering Technology Research Center for Data Science 2016KF09.
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Du, J., Xu, R., Gui, L., Wang, X. (2018). Leveraging Target-Oriented Information for Stance Classification. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_3
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