Abstract
The problem of identifying and correctly attributing speaker stance in human communication is addressed in this paper. The data set consists of political blogs dealing with the 2016 British referendum. A cognitive-functional framework is adopted with data annotated for six notional stance categories: contrariety, hypotheticality, necessity, prediction, source of knowledge, and uncertainty. We show that these categories can be implemented in a text classification task and automatically detected. To this end, we propose a large set of lexical and syntactic linguistic features. These features were tested and classification experiments were implemented using different algorithms. We achieved accuracy of up to 30% for the six-class experiments, which is not fully satisfactory. As a second step, we calculated the pair-wise combinations of the stance categories. The contrariety and necessity binary classification achieved the best results with up to 71% accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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, June 2011
Bird, S.: NLTK: the natural language toolkit. In: Proceedings of the COLING/ACL on Interactive presentation sessions, pp. 67–72. Association for Computational Linguistics (2006)
Du Bois, J.W.: The stance triangle. In: Englebretson, R. (ed.) Stance taking in discourse: Subjectivity, evaluation, interaction, pp. 139–182 (2007)
Englebretson, R. (ed.): Stancetaking in Discourse: Subjectivity, Evaluation, Interaction, vol. 164. John Benjamins Publishing (2007)
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. Association for Computational Linguistics, June 2016
Gavagai, A.P.I.: https://developer.gavagai.se
Hasan, K.S., Ng, V.: Stance classification of ideological debates: data, models, features, and constraints. In: IJCNLP, pp. 1348–1356 (2013)
Hasan, K.S., Ng, V.: Frame semantics for stance classification. In: CoNLL, pp. 124–13 (2013)
Hasan, K.S., Ng, V.: Extra-linguistic constraints on stance recognition in ideological debates. ACL 2, 124–132 (2013c)
Hasan, K.S., Ng, V.: Why are you taking this stance? identifying and classifying reasons in ideological debates. In: EMNLP, pp. 751–762, March 2014
Kucher, K., Kerren, A., Paradis, C., Sahlgren, M.: Visual analysis of text annotations for stance classification with ALVA. In: Poster Abstracts of the EG/VGTC Conference on Visualization (EuroVis 2016), pp. 49–51. Eurographics-European Association for Computer Graphics (2016)
Kucher, K., Schamp-Bjerede, T., Kerren, A., Paradis, C., Sahlgren, M.: Visual analysis of online social media to open up the investigation of stance phenomena. Inf. Visual. 15(2), 93–116 (2016b)
Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. arXiv preprint arXiv:1605.01655 (2016)
Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp. 93–116. Association for Computational Linguistics, July 2004
Rajadesingan, A., Liu, H.: Identifying users with opposing opinions in Twitter debates. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds.) SBP 2014. LNCS, vol. 8393, pp. 153–160. Springer, Cham (2014). doi:10.1007/978-3-319-05579-4_19
Simaki, V., Paradis, C., Skeppstedt, M., Sahlgren, M., Kucher, K., Kerren, A.: Annotating speaker stance in discourse: the Brexit Blog Corpus, forthcoming (2017)
Simaki, V., Mporas, I., Megalooikonomou, V.: Evaluation and sociolinguistic analysis of text features for gender and age identification. Am. J. Eng. Appl. Sci. 9(4), 868–876 (2016)
Simaki, V., Mporas, I., Megalooikonomou, V.: Age identification of twitter users: classification methods and sociolinguistic analysis. In: Proceedings of the 17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2016 (2016, to appear)
Somasundaran, S., Wiebe, J.: Recognizing stances in ideological on-line debates. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 116–124. Association for Computational Linguistics, June 2010
Sridhar, D., Getoor, L., Walker, M.: Collective stance classification of posts in online debate forums. In: Proceedings of the Association for Computational Linguistics, ACL 2014, p. 109 (2014)
StaViCTA project. http://cs.lnu.se/stavicta/
Taboada, M.: Sentiment analysis: an overview from linguistics. Ann. Rev. Linguist. 2, 325–347 (2016)
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)
Walker, M.A., Anand, P., Abbott, R., Tree, J.E.F., Martell, C., King, J.: That is your evidence?: Classifying stance in online political debate. Decis. Support Syst. 53(4), 719–729 (2012)
Walker, M.A., Tree, J.E.F., Anand, P., Abbott, R., King, J.: A corpus for research on deliberation and debate. In: LREC, pp. 812–817 (2012)
Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Comput. Linguist. 30(3), 277–308 (2004)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)
Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing-style features and classification techniques. J. Am. Soc. Inform. Sci. Technol. 57(3), 378–393 (2006)
Acknowledgments
This research is part of the StaViCTA project [22], supported by the Swedish Research Council (framework grant “the Digitized Society – Past, Present, and Future”, No. 2012-5659). We thank Maria Skeppstedt and Tom Sköld for the data annotation, and Kostiantyn Kucher for the implementation of the annotation tool.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Simaki, V., Paradis, C., Kerren, A. (2017). Stance Classification in Texts from Blogs on the 2016 British Referendum. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_70
Download citation
DOI: https://doi.org/10.1007/978-3-319-66429-3_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66428-6
Online ISBN: 978-3-319-66429-3
eBook Packages: Computer ScienceComputer Science (R0)