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English Information

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Authors
# Name
1 Marcelo Dias(marcelo.dias@inf.ufrgs.br)
2 Karin Becker(karin.becker@inf.ufrgs.br)

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Reference
# Reference
1 Anand, P., Walker, M., Abbott, R., Tree, J. E. F., Bowmani, R., e Minor, M. (2011). Cats rule and dogs drool!: Classifying stance in online debate. Em Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis, pgs. 1–9. Association for Computational Linguistics.
2 Dias, M. e Becker, K. (2016). INF-UFRGS-OPINION-MINING: Automatic generation of a training corpus for unsupervised identification of stance in tweets. Em Proceedings of the International Workshop on Semantic Evaluation - Task 6, SemEval ’16, San Diego, CA, USA. To appear.
3 Faulkner, A. (2014). Automated classification of stance in student essays: An approach using stance target information and the wikipedia link-based measure. Em Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014, Pensacola Beach, Florida, May 21-23, 2014.
4 Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1–167.
5 Mohammad, S. M., Kiritchenko, S., Sobhani, P., Zhu, X., e Cherry, C. (2016). Semeval-2016 task 6: Detecting stance in tweets. Em Proceedings of the International Workshop on Semantic Evaluation, SemEval ’16, San Diego, California.
6 Mohammad, S. M., Zhu, X., Kiritchenko, S., e Martin, J. (2015). Sentiment, emotion, purpose, and style in electoral tweets. Information Processing & Management, 51(4):480–499.
7 Rajadesingan, A. e Liu, H. (2014). Identifying users with opposing opinions in twitter debates. Em Social Computing, Behavioral-Cultural Modeling and Prediction, pgs. 153–160. Springer.
8 Somasundaran, S. e Wiebe, J. (2009). Recognizing stances in online debates. Em 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: Volume 1-Volume 1, pgs. 226–234. Association for Computational Linguistics.
9 Tang, J., Nobata, C., Dong, A., Chang, Y., e Liu, H. (2015). Propagation-based sentiment analysis for microblogging data. Em Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30 - May 2, 2015, pgs. 577–585.
10 Thomas, M., Pang, B., e Lee, L. (2006). Get out the vote: Determining support or opposition from congressional floor-debate transcripts. Em Proceedings of the 2006 conference on empirical methods in natural language processing, pgs. 327–335. Association for Computational Linguistics.