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Frontiers of Computer Science

, Volume 13, Issue 1, pp 127–138 | Cite as

Stance detection via sentiment information and neural network model

  • Qingying Sun
  • Zhongqing Wang
  • Shoushan Li
  • Qiaoming ZhuEmail author
  • Guodong Zhou
Research Article
  • 55 Downloads

Abstract

Stance detection aims to automatically determine whether the author is in favor of or against a given target. In principle, the sentiment information of a post highly influences the stance. In this study, we aim to leverage the sentiment information of a post to improve the performance of stance detection. However, conventional discrete models with sentimental features can cause error propagation. We thus propose a joint neural network model to predict the stance and sentiment of a post simultaneously, because the neural network model can learn both representation and interaction between the stance and sentiment collectively. Specifically, we first learn a deep shared representation between stance and sentiment information, and then use a neural stacking model to leverage sentimental information for the stance detection task. Empirical studies demonstrate the effectiveness of our proposed joint neural model.

Keywords

natural language processing machine learning stance detection 

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Notes

Acknowledgements

In building our system, we are grateful to all the people who have helped us. Jingjing Wang, Lu Zhang, Dong Zhang, and Suyang Zhu have provided help in programing; Zhenghua Li, Xing Wang, and Ziwei Fan have given us insightful comments etc. We also would like to thank the organizer of SemEval-2016 Task 6 for the hard work, especially in data annotation.

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61331011, 61751206, 61773276, 61672366), Jiangsu Provincial Science and Technology Plan (BK20151222), Project of Natural Science Research of the Universities of Jiangsu Province (16KJB520007) and Huaiyin Normal University Youth Talent Support Program (13HSQNZ07).

Supplementary material

11704_2018_7150_MOESM1_ESM.ppt (204 kb)
Stance Detection via Sentiment Information and Neural Network Model

References

  1. 1.
    Mohammad S M, 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. 2016, 31–41Google Scholar
  2. 2.
    Somasundaran S, Wiebe J. Recognizing stances in online debates. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009, 226–234Google Scholar
  3. 3.
    Murakami A, Raymond R. Support or oppose?: classifying positions in online debates from reply activities and opinion expressions. In: Proceedings of the 23rd International Conference on Computational Linguistics. 2010, 869–875Google Scholar
  4. 4.
    Anand P, Walker M, Abbott R, Tree J E, 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. 2011, 1–9Google Scholar
  5. 5.
    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. 2012, 592–596Google Scholar
  6. 6.
    Hasan K S, Ng V. Stance classification of ideological debates: data, models, features, and constraints. In: Proceedings of the International Joint Conference on Natural Language Processing. 2013, 1348–1356Google Scholar
  7. 7.
    Sun Q, Wang Z, Zhu Q, Zhou G. Exploring various linguistic features for stance detection. In: Proceedings of the International Conference on Computer Processing of Oriental Languages. 2016, 840–847Google Scholar
  8. 8.
    Mohammad S M, Sobhani P, Kiritchenko S. Stance and sentiment in tweets. 2016, arXiv preprint arXiv:1605.01655Google Scholar
  9. 9.
    Thomas M, Pang B, Lee L. Get out the vote: determining support or opposition from congressional floor-debate transcripts. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. 2006, 327–335Google Scholar
  10. 10.
    Bansal M, Cardie C, Lee L. The power of negative thinking: exploiting label disagreement in the min-cut classification framework. COLING 2008: Companion Volume: Posters. 2008, 15–18Google Scholar
  11. 11.
    Burfoot C, Bird S, Baldwin T. Collective classification of congressional floor-debate transcripts. In: Proceedings of the 49th AnnualMeeting of the Association for Computational Linguistics. 2011, 1506–1515Google Scholar
  12. 12.
    Agrawal R, Rajagopalan S, Srikant R, Xu Y. Mining newsgroups using networks arising from social behavior. In: Proceedings of the 12th International Conference on World Wide Web. 2003, 529–535Google Scholar
  13. 13.
    Sridhar D, Getoor L, Walker M. Collective stance classification of posts in online debate forums. In: Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media. 2014, 109–117CrossRefGoogle Scholar
  14. 14.
    Johnson K, Goldwasser D. Identifying stance by analyzing political discourse on twitter. In: Proceedings of EMNLP Workshop on Natural Language Processing and Computational Social Science. 2016, 66–75Google Scholar
  15. 15.
    Volkova S, Bachrach Y, Armstrong M, Sharma V. Inferring latent user properties from texts published in social media. In: Proceedings of Association for the Advancement of Artificial Intelligence. 2015, 4296–4297Google Scholar
  16. 16.
    Lukasik M, Srijith P K, Vu D, Bontcheva K, Zubiaga A, Cohn T. Hawkes processes for continuous time sequence classification: an application to rumour stance classification in twitter. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016, 393–398Google Scholar
  17. 17.
    Zubiaga A, Kochkina E, Liakata M, Procter R, Lukasik M. Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. 2016, arXiv preprint arXiv:1609.09028Google Scholar
  18. 18.
    Rajadesingan A, Liu H. Identifying users with opposing opinions in Twitter debates. In: Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. 2014, 153–160CrossRefGoogle Scholar
  19. 19.
    Mohammad S M, Kiritchenko S, Sobhani P, Zhu X, Cherry C. A dataset for detecting stance in tweets. In: Proceedings of the 10th edition of the the Language Resources and Evaluation Conference (LREC). 2016, 3945–3952Google Scholar
  20. 20.
    Zarrella G, Marsh A. MITRE at semeval-2016 task 6: transfer learning for stance detection. In: Proceedings of the 10th International Workshop on Semantic Evaluation. 2016, 458–463Google Scholar
  21. 21.
    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: Proceedings of the 10th International Workshop on Semantic Evaluation. 2016, 384–388Google Scholar
  22. 22.
    Pang B, Lee L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2002, 79–86Google Scholar
  23. 23.
    Yessenalina A, Yue Y, Cardie C. Multi-level structured models for document-level sentiment classification. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. 2010, 1046–1056Google Scholar
  24. 24.
    Brychcın T, Habernal I. Unsupervised improving of sentiment analysis using global target context. In: Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP. 2013, 122–128Google Scholar
  25. 25.
    Tang D, Qin B, Liu T. Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2015, 1422–1432CrossRefGoogle Scholar
  26. 26.
    Khattri A, Joshi A, Bhattacharyya P, Carman M J. Your sentiment precedes you: using an author’s historical tweets to predict sarcasm. In: Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 2015, 25–30CrossRefGoogle Scholar
  27. 27.
    Saif H, He Y, Fernandez M, Alani H. Contextual semantics for sentiment analysis of Twitter. Information Processing & Management. 2016, 52(1): 5–19Google Scholar
  28. 28.
    Sobhani P, Mohammad S M, Kiritchenko S. Detecting stance in tweets and analyzing its interaction with sentiment. In: Proceedings of the 5th Joint Conference on Lexical and Computational Semantics. 2016, 159–169CrossRefGoogle Scholar
  29. 29.
    Titov I, McDonald R T. A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2008, 308–316Google Scholar
  30. 30.
    Watanabe Y, Asahara M, Matsumoto Y. A structured model for joint learning of argument roles and predicate senses. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2010, 98–102Google Scholar
  31. 31.
    Simova I, Vasilev D, Popov A, Simov K, Osenova P. Joint ensemble model for POS tagging and dependency parsing. In: Proceedings of the 1st Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages. 2014, 15–25Google Scholar
  32. 32.
    Socher R, Perelygin A, Wu J Y, Chuang J, Manning C D, Ng A Y, Potts C. Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2013, 1631–1642Google Scholar
  33. 33.
    Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. Journal ofMachine Learning Research. 2011, 12(Aug): 2493–2537Google Scholar
  34. 34.
    Liu Y, Li S, Zhang X, Sui Z. Implicit discourse relation classification via multi-task neural networks. 2016, arXiv preprint arXiv:1603.02776Google Scholar
  35. 35.
    Zhou J, Xu W. End-to-end learning of semantic role labeling using recurrent neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015, 1127–1137Google Scholar
  36. 36.
    Chen H, Zhang Y, Liu Q. Neural network for heterogeneous annotations. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2016, 731–741CrossRefGoogle Scholar
  37. 37.
    Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735–1780CrossRefGoogle Scholar
  38. 38.
    Graves A. Generating sequences with recurrent neural networks. 2013, arXiv preprint arXiv:1308.0850Google Scholar
  39. 39.
    Johnson R, Zhang T. Effective use of word order for text categorization with convolutional neural networks. 2014, arXiv preprint arXiv:1412.1058Google Scholar
  40. 40.
    Srivastava N, Hinton G E, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(1): 1929–1958MathSciNetzbMATHGoogle Scholar
  41. 41.
    Tieleman T, Hinton G. Rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning. Technical Report, 2012Google Scholar
  42. 42.
    Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference on Artifical Intelligence and Statistics. 2010, 249–256Google Scholar
  43. 43.
    Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. 2013, arXiv preprint arXiv:1301.3781Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Qingying Sun
    • 1
    • 2
  • Zhongqing Wang
    • 1
  • Shoushan Li
    • 1
  • Qiaoming Zhu
    • 1
    Email author
  • Guodong Zhou
    • 1
  1. 1.Natural Language Processing LabSoochow UniversitySuzhouChina
  2. 2.Huaiyin Normal UniversityHuai’anChina

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