SMACk: An Argumentation Framework for Opinion Mining

Mauro Dragoni 1, * Célia Da Costa Pereira 2 Andrea G. B. Tettamanzi 3 Serena Villata 3
* Auteur correspondant
1 FBK - Fondazione Bruno Kessler [Trento, Italy]
2 SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : The extraction of the relevant and debated opinions from online social media and commercial websites is an emerging task in the opinion mining research field. Its growing relevance is mainly due to the impact of exploiting such techniques in different application domains from social science analysis to personal advertising. In this demo, we present our opinion summary application built on top of an ar-gumentation framework, a standard AI framework whose value is to exchange, communicate and resolve possibly conflicting viewpoints in distributed scenarios. We show how our application is able to extract relevant and debated opinions from a set of documents containing user-generated content from online commercial websites.
Keywords : opinion mining argumentation
Type de document :
Communication dans un congrès
Subbarao Kambhampati. International Joint Conference on Artificial Intelligence (IJCAI), Jul 2016, New York, NY, United States. IJCAI/AAAI Press, pp.4242-4243, 2016, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016, New York, NY, USA, 9--15 July 2016. 〈http://www.ijcai.org〉
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Mauro Dragoni, Célia Da Costa Pereira, Andrea G. B. Tettamanzi, Serena Villata. SMACk: An Argumentation Framework for Opinion Mining. Subbarao Kambhampati. International Joint Conference on Artificial Intelligence (IJCAI), Jul 2016, New York, NY, United States. IJCAI/AAAI Press, pp.4242-4243, 2016, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016, New York, NY, USA, 9--15 July 2016. 〈http://www.ijcai.org〉. 〈hal-01353931〉

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