Não se perca no debate! Mineração de Argumentação em Redes Sociais
Resumo
Mineração de Argumentação é uma área de pesquisa entre Linguística Computacional e Ciência de Dados, com grande importância para processos de tomada de decisão, possibilitando a identificação e recuperação da estrutura do diálogo e da polaridade dos argumentos usados pelos participantes. Este trabalho é um primeiro passo para construir um processo de análise de discussões em redes sociais com base na Mineração de Argumentação visando compreender a estrutura da discussão que se estabelece nas redes. Os resultados obtidos nesse processo são usados para alimentar a ferramenta de visualização de discussões Visu, de modo a facilitar o acompanhamento e entendimento das discussões em redes sociais.
Palavras-chave:
mineração de argumentação, análise de sentimentos, redes sociais, visualização de discussões
Referências
Abbott, R., Ecker, B., Anand, P. e Walker, M. (2016) “Internet argument corpus 2.0: An sql schema for dialogic social media and the corpora to go with it.” In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) (pp. 4445-4452), https://www.aclweb.org/anthology/L16-1704/
Agarwal, A., Xie, B., Vovsha, I., Rambow, O. e Passonneau, R. (2011) “Sentiment analysis of twitter data.” In Proceedings of the Workshop on Languages in Social Media, LSM’11, https://dl.acm.org/doi/10.5555/2021109.2021114
ArgMining (2021) “The 8th Workshop on Argumentation Mining”, https://2021.argmining.org/
Boe, B. e Payne, J. (2016) "PRAW: The Python Reddit API Wrapper" GitHub. https://github.com/praw-dev/praw
Burgess Yakemovic, K. C. e Conklin, E. J. (1990) “Report on a development project use of an issue-based information system.” In Proceedings of the 1990 ACM conference on Computer-supported cooperative work (pp. 105-118), https://doi.org/10.1145/99332.99347
Dusmanu, M., Cabrio, E. e Villata, S. (2017) “Argument mining on Twitter: Arguments, facts and sources." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 2317-2322), http://dx.doi.org/10.18653/v1/D17- 1245
Garcia, A. C. e Klein, M. (2015) “Making sense of large-group discussion using automatically generated RST-based explanations”. Available at SSRN 2554838, http://dx.doi.org/10.2139/ssrn.2554838
IBM (2020) “Project Debater”, https://www.research.ibm.com/artificialintelligence/project-debater/index.html
Lawrence, J., Bex, F., Reed, C. e Snaith, M. (2012) “AIFdb: Infrastructure for the Argument Web.” In COMMA (pp. 515-516), https://doi.org/10.3233/978-1-61499- 111-3-515.
Lawrence, J. e Reed, C. (2020) “Argument mining: A survey.” Computational Linguistics, 45(4), 765-818, https://doi.org/10.1162/coli_a_00364
Lippi, M. e Torroni, P. (2016) “Argument Mining: State of the art and emerging trends.” ACM Transactions on Internet Technology, 16 (2), 2016, http://dx.doi.org/10.1145/2850417
Lytos, A., Lagkas, T., Sarigiannidis, P. e Bontcheva, K. (2019) “The evolution of argumentation mining: From models to social media and emerging tools.” Information Processing & Management, 56(6), 102055, https://doi.org/10.1016/j.ipm.2019.102055
Maas, A., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y. e Potts, C. (2011) “Learning word vectors for sentiment analysis”. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (pp. 142- 150), https://www.aclweb.org/anthology/P11-1015
Nascimento, R. S., Nascimento, G., Carvalho, F. e Guedes, G. (2020). “Mineração de Opiniões com LIWC: abordagem prática sobre sistemas judiciais eletrônicos brasileiros”. In Anais do IX Brazilian Workshop on Social Network Analysis and Mining (pp. 132-141). SBC, https://doi.org/10.5753/brasnam.2020.11169
NLTK (2021) “Natural Language Toolkit.”, https://www.nltk.org/
Palau, R. M. e Moens, M. F. (2009) “Argumentation mining: the detection, classification and structure of arguments in text”. In Proceedings of the 12th international conference on artificial intelligence and law (pp. 98-107), https://doi.org/10.1145/1568234.1568246
Paltoglou, G. e Thelwall, M. (2012) “Twitter, MySpace, Digg: Unsupervised sentiment analysis in social media”. ACM Transactions on Intelligent Systems and Technology (TIST), 3(4), 1-19, https://doi.org/10.1145/2337542.2337551
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... e Duchesnay, E. (2011) “Scikit-learn: Machine learning in Python". The Journal of machine Learning research, 12, 2825-2830, https://dl.acm.org/doi/10.5555/1953048.2078195
Rodrigues, A. O., de Melo-Minardi, R. C. e Junior, W. M. (2013) “Como nos Sentimos: Uma Ferramenta de Mineração Visual de Sentimentos no Twitter”. In Anais do II Brazilian Workshop on Social Network Analysis and Mining (pp. 91-102). SBC, https://sol.sbc.org.br/index.php/brasnam/article/view/6836
Skoric, M. M., Zhu, Q., Goh, D. e Pang, N. (2016) “Social media and citizen engagement: A meta-analytic review”. New Media & Society, 18(9), 1817-1839. http://dx.doi.org/10.1177/1461444815616221.
Tavares, R. L, Pimentel, M., Araujo, R. M. (2012) “Visualização de Informações em Discussões Políticas". In: Brazilian Symposium on Collaborative Systems. São Paulo. Sociedade Brasileira de Computação. pp. ---. http://sws2012.ime.usp.br/sbsc/SBSC2012/data/4890a075.pdf
Tavares, R. L, Pimentel, M., Araujo, R. M. (2014) “Visualizing Clashes and Alliances in Social Networks of Political Discussions”. Social Networking, v. 03, p. 94-101. http://dx.doi.org/10.4236/sn.2014.32012
Toulmin, S. E. (2003). “The uses of argument”. Cambridge university press. https://doi.org/10.1080/00048405985200191
UFRJ. (2020) “Media Lab”, https://medialabufrj.net/
Walker, M. A., Tree, J. E. F., Anand, P., Abbott, R. e King, J. (2012) “A Corpus for Research on Deliberation and Debate.” In LREC (Vol. 12, pp. 812-817), https://www.aclweb.org/anthology/L12-1643/
Zhai, C. e Massung, S. (2016) "Opinion Mining and Sentiment Analysis. Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining. "Association for Computing Machinery and Morgan & Claypool, https://doi.org/10.1145/2915031.2915050
Agarwal, A., Xie, B., Vovsha, I., Rambow, O. e Passonneau, R. (2011) “Sentiment analysis of twitter data.” In Proceedings of the Workshop on Languages in Social Media, LSM’11, https://dl.acm.org/doi/10.5555/2021109.2021114
ArgMining (2021) “The 8th Workshop on Argumentation Mining”, https://2021.argmining.org/
Boe, B. e Payne, J. (2016) "PRAW: The Python Reddit API Wrapper" GitHub. https://github.com/praw-dev/praw
Burgess Yakemovic, K. C. e Conklin, E. J. (1990) “Report on a development project use of an issue-based information system.” In Proceedings of the 1990 ACM conference on Computer-supported cooperative work (pp. 105-118), https://doi.org/10.1145/99332.99347
Dusmanu, M., Cabrio, E. e Villata, S. (2017) “Argument mining on Twitter: Arguments, facts and sources." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 2317-2322), http://dx.doi.org/10.18653/v1/D17- 1245
Garcia, A. C. e Klein, M. (2015) “Making sense of large-group discussion using automatically generated RST-based explanations”. Available at SSRN 2554838, http://dx.doi.org/10.2139/ssrn.2554838
IBM (2020) “Project Debater”, https://www.research.ibm.com/artificialintelligence/project-debater/index.html
Lawrence, J., Bex, F., Reed, C. e Snaith, M. (2012) “AIFdb: Infrastructure for the Argument Web.” In COMMA (pp. 515-516), https://doi.org/10.3233/978-1-61499- 111-3-515.
Lawrence, J. e Reed, C. (2020) “Argument mining: A survey.” Computational Linguistics, 45(4), 765-818, https://doi.org/10.1162/coli_a_00364
Lippi, M. e Torroni, P. (2016) “Argument Mining: State of the art and emerging trends.” ACM Transactions on Internet Technology, 16 (2), 2016, http://dx.doi.org/10.1145/2850417
Lytos, A., Lagkas, T., Sarigiannidis, P. e Bontcheva, K. (2019) “The evolution of argumentation mining: From models to social media and emerging tools.” Information Processing & Management, 56(6), 102055, https://doi.org/10.1016/j.ipm.2019.102055
Maas, A., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y. e Potts, C. (2011) “Learning word vectors for sentiment analysis”. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (pp. 142- 150), https://www.aclweb.org/anthology/P11-1015
Nascimento, R. S., Nascimento, G., Carvalho, F. e Guedes, G. (2020). “Mineração de Opiniões com LIWC: abordagem prática sobre sistemas judiciais eletrônicos brasileiros”. In Anais do IX Brazilian Workshop on Social Network Analysis and Mining (pp. 132-141). SBC, https://doi.org/10.5753/brasnam.2020.11169
NLTK (2021) “Natural Language Toolkit.”, https://www.nltk.org/
Palau, R. M. e Moens, M. F. (2009) “Argumentation mining: the detection, classification and structure of arguments in text”. In Proceedings of the 12th international conference on artificial intelligence and law (pp. 98-107), https://doi.org/10.1145/1568234.1568246
Paltoglou, G. e Thelwall, M. (2012) “Twitter, MySpace, Digg: Unsupervised sentiment analysis in social media”. ACM Transactions on Intelligent Systems and Technology (TIST), 3(4), 1-19, https://doi.org/10.1145/2337542.2337551
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... e Duchesnay, E. (2011) “Scikit-learn: Machine learning in Python". The Journal of machine Learning research, 12, 2825-2830, https://dl.acm.org/doi/10.5555/1953048.2078195
Rodrigues, A. O., de Melo-Minardi, R. C. e Junior, W. M. (2013) “Como nos Sentimos: Uma Ferramenta de Mineração Visual de Sentimentos no Twitter”. In Anais do II Brazilian Workshop on Social Network Analysis and Mining (pp. 91-102). SBC, https://sol.sbc.org.br/index.php/brasnam/article/view/6836
Skoric, M. M., Zhu, Q., Goh, D. e Pang, N. (2016) “Social media and citizen engagement: A meta-analytic review”. New Media & Society, 18(9), 1817-1839. http://dx.doi.org/10.1177/1461444815616221.
Tavares, R. L, Pimentel, M., Araujo, R. M. (2012) “Visualização de Informações em Discussões Políticas". In: Brazilian Symposium on Collaborative Systems. São Paulo. Sociedade Brasileira de Computação. pp. ---. http://sws2012.ime.usp.br/sbsc/SBSC2012/data/4890a075.pdf
Tavares, R. L, Pimentel, M., Araujo, R. M. (2014) “Visualizing Clashes and Alliances in Social Networks of Political Discussions”. Social Networking, v. 03, p. 94-101. http://dx.doi.org/10.4236/sn.2014.32012
Toulmin, S. E. (2003). “The uses of argument”. Cambridge university press. https://doi.org/10.1080/00048405985200191
UFRJ. (2020) “Media Lab”, https://medialabufrj.net/
Walker, M. A., Tree, J. E. F., Anand, P., Abbott, R. e King, J. (2012) “A Corpus for Research on Deliberation and Debate.” In LREC (Vol. 12, pp. 812-817), https://www.aclweb.org/anthology/L12-1643/
Zhai, C. e Massung, S. (2016) "Opinion Mining and Sentiment Analysis. Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining. "Association for Computing Machinery and Morgan & Claypool, https://doi.org/10.1145/2915031.2915050
Publicado
18/07/2021
Como Citar
SOUSA, João Pedro da Silva; NASCIMENTO, Rodrigo Costa Uchoa do; ARAUJO, Renata Mendes de; COELHO, Orlando Bisacchi.
Não se perca no debate! Mineração de Argumentação em Redes Sociais. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 10. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 139-150.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2021.16132.
