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Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention

  • Kuntal Dey
  • Ritvik Shrivastava
  • Saroj Kaushik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)

Abstract

The topical stance detection problem addresses detecting the stance of the text content with respect to a given topic: whether the sentiment of the given text content is in favor of (positive), is against (negative), or is none (neutral) towards the given topic. Using the concept of attention, we develop a two-phase solution. In the first phase, we classify subjectivity - whether a given tweet is neutral or subjective with respect to the given topic. In the second phase, we classify sentiment of the subjective tweets (ignoring the neutral tweets) - whether a given subjective tweet has a favor or against stance towards the topic. We propose a Long Short-Term memory (LSTM) based deep neural network for each phase, and embed attention at each of the phases. On the SemEval 2016 stance detection Twitter task dataset [7], we obtain a best-case macro F-score of 68.84% and a best-case accuracy of 60.2%, outperforming the existing deep learning based solutions. Our framework, T-PAN, is the first in the topical stance detection literature, that uses deep learning within a two-phase architecture.

References

  1. 1.
    Augenstein, I., Rocktäschel, T., Vlachos, A., Bontcheva, K.: Stance Detection with Bidirectional Conditional Encoding. arXiv preprint arXiv:1606.05464 (2016)
  2. 2.
    Boltuzic, F., Karan, M., Alagic, D., Šnajder, J.: Takelab at SemEval-2016 task 6: stance classification in tweets using a genetic algorithm based ensemble. In: SemEval, pp. 464–468 (2016)Google Scholar
  3. 3.
    Du, J., Xu, R., He, Y., Gui, L.: Stance classification with target-specific neural attention networks. In: IJCAI, pp. 3988–3994 (2017)Google Scholar
  4. 4.
    Elfardy, H., Diab, M.: CU-GWU perspective at SemEval-2016 task 6: ideological stance detection in informal text. In: SemEval, pp. 434–439 (2016)Google Scholar
  5. 5.
    Liu, C., Li, W., Demarest, B., Chen, Y., Couture, S., Dakota, D., Haduong, N., Kaufman, N., Lamont, A., Pancholi, M., et al.: IUCL at SemEval-2016 task 6: an ensemble model for stance detection in twitter. In: SemEval, pp. 394–400 (2016)Google Scholar
  6. 6.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781 (2013)
  7. 7.
    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)Google Scholar
  8. 8.
    Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and Sentiment in Tweets. arXiv preprint arXiv:1605.01655 (2016)
  9. 9.
    Rosenthal, S., Ritter, A., Nakov, P., Stoyanov, V.: SemEval-2014 task 9: sentiment analysis in twitter. In: SemEval 2014, pp. 73–80 (2014)Google Scholar
  10. 10.
    Taulé, M., Martí, M.A., Rangel, F.M., Rosso, P., Bosco, C., Patti, V., et al.: Overview of the task on stance and gender detection in tweets on Catalan independence at IberEval 2017. In: IberEval, CEUR-WS, vol. 1881, pp. 157–177 (2017)Google Scholar
  11. 11.
    Vijayaraghavan, P., Sysoev, I., Vosoughi, S., Roy, D.: Deepstance at SemEval-2016 task 6: detecting stance in tweets using character and word-level CNNs. arXiv preprint arXiv:1606.05694 (2016)
  12. 12.
    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, pp. 384–388 (2016)Google Scholar
  13. 13.
    Wojatzki, M., Zesch, T.: ltl.uni-due at SemEval-2016 task 6: stance detection in social media using stacked classifiers. In: SemEval, pp. 428–433 (2016)Google Scholar
  14. 14.
    Zarrella, G., Marsh, A.: Mitre at SemEval-2016 Task 6: Transfer Learning for Stance Detection. arXiv preprint arXiv:1606.03784 (2016)
  15. 15.
    Zhang, Z., Lan, M.: ECNU at SemEval-2016 task 6: relevant or not? supportive or not? a two-step learning system for automatic detecting stance in tweets. In: SemEval, pp. 451–457 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IBM ResearchNew DelhiIndia
  2. 2.NSITNew DelhiIndia
  3. 3.IIT DelhiNew DelhiIndia

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