Affiliations: [a] Institut für Kognitionswissenschaft, Universität Osnabrück, Osnabrück, Germany | [b] Artificial Intelligence Research and Development Laboratory, Department of Computer Science and Engineering, Universidad Nacional del Sur, Av. Alem 1253, (8000) Bahía Blanca, Argentina | [c] Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Rivadavia 1917, Caba, Buenos Aires, Argentina
Abstract: Social networks have grown exponentially in use and impact on the society as a whole. In particular, microblogging platforms such as Twitter have become important tools to assess public opinion on different issues. Recently, some approaches for assessing Twitter messages have been developed, identifying sentiments associated with relevant keywords or hashtags. However, such approaches have an important limitation, as they do not take into account contradictory and potentially inconsistent information which might emerge from relevant messages. We contend that the information made available in Twitter can be useful to extract a particular version of arguments (called “opinions” in our formalization) which emerge bottom-up from the social interaction associated with such messages. In this paper we present a novel framework which allows to mine opinions from Twitter based on incrementally generated queries. As a result, we will be able to obtain an “opinion tree”, rooted in the first original query. Distinguished, conflicting elements in an opinion tree lead to so-called “conflict trees”, which resemble dialectical trees as those used traditionally in defeasible argumentation.11This paper extends preliminary work [11] presented at the First International Conference on Agreement Technologies (AT 2012), held in Dubrovnik, Croatia, in October 2012.
Keywords: Argumentation, opinion mining, social media
