Abstract
The task of stance detection is to determine whether someone is in favor or against a certain topic. A person may express the same stance towards a topic using positive or negative words. In this paper, several features and classifiers are explored to find out the combination that yields the best performance for stance detection. Due to the large number of features, ReliefF feature selection method was used to reduce the large dimensional feature space and improve the generalization capabilities. Experimental analyses were performed on five datasets, and the obtained results revealed that a majority vote classifier of the three classifiers: Random Forest, linear SVM and Gaussian Naïve Bayes classifiers can be adopted for stance detection task.
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References
Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: SemEval-2016 Task 6: detecting stance in tweets. In: Proceedings of SemEval, pp. 31–41 (2016)
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent Twitter sentiment classification. In: Proceedings of ACL, pp. 151–160 (2011)
Augenstein, I., Rocktäschel, T., Vlachos, A., Bontcheva, K.: Stance detection with bidirectional conditional encoding. In: Proceedings of EMNLP, pp. 876–885 (2016)
Mohammad, S., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. Spec. Sect. ACM Trans. Internet Technol. Argum. Soc. Media , 17(3), 26 (2017)
Lai, M., Farías, D.I.H., Patti, V., Rosso, P.: Friends and enemies of Clinton and Trump: using context for detecting stance in political tweets. In: Mexican International Conference on Artificial Intelligence, pp. 155–168 (2016)
Ebrahimi, J., Dou, D., Lowd, D.: Weakly supervised tweet stance classification by relational bootstrapping. In: Proceedings of EMNLP, pp. 1012–1017 (2016)
Ebrahimi, J., Dou, D., Lowd, D.: A joint sentiment-target-stance model for stance classification in tweets. In: Proceedings of COLING, pp. 2656–2665 (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)
Zarrella, G., Marsh, A.: MITRE at SemEval-2016 Task 6: transfer learning for stance detection. In: Proceedings of SemEval, pp. 458–463 (2016)
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 SemEval, pp. 384–388 (2016)
Li, J., Luong, T., Jurafsky, D.: A hierarchical neural autoencoder for paragraphs and documents. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1, pp. 1106–1115 (2015)
Mikolov, T., Chen, K., Corrado, G., Dean, G.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)
Hutto, C.J., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International Conference on Weblogs and Social Media (ICWSM), pp. 216–255 (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Kononenko, I., Šimec, E., Robnik-Šikonja, M.: Overcoming the myopia of inductive learning algorithms with RELIEFF. J. Appl. Intell. 7(1), 39–55 (1997)
Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., Schneider, N., Smith, N.A.: Improved part-of-speech tagging for online conversational text with word clusters. In: HLT-NAACL, pp. 380–390 (2013)
Loper, E., Bird, S.: NLTK: the natural language toolkit. In: Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pp. 63–70. ACL (2002)
Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 1, pp. 423–430 (2003)
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: Proceedings of SemEval, pp. 451–457 (2016)
Stone, P., Dumphy, D., Smith, M., Ogilvie, D.: The General Inquirer: A Computer Approach to Content Analysis. MIT Studies in Comparative Politics. MIT Press, Cambridge (1966)
Nielsen, F.Å.: A new ANEW: evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903 (2011)
Whissell, C.: Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural language. Psychol. Rep. 105(2), 509–521 (2009)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. Association for Computational Linguistics (2005)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC), Valletta, Malta, pp. 2200–2204. European Language Resources Association (ELRA) (2010)
Balikas, G., Amini, M.R.: TwiSE at SemEval-2016 Task 4: Twitter sentiment classification. In: Proceedings of SemEval, pp. 85–91 (2016)
Kiritchenko, S., Zhu, X., Mohammad, S.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014)
Mohammad, S., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of SemEval, pp. 321–327 (2013)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
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Mourad, S.S., Shawky, D.M., Fayed, H.A., Badawi, A.H. (2018). Stance Detection in Tweets Using a Majority Vote Classifier. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_37
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