An Argument-based Approach to Mining Opinions from Twitter

KATHRIN GROSSE; CARLOS IVÁN CHESÑEVAR; ANA MAGUITMAN

Dubrovniik

Conferencia; First Intl. Conf on Agreement Technologies; 2012

CETINA, Spain

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 dierent 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 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 rst original query. Distinguished, con icting elements in an opinion tree lead to so-called con ict trees", which resemble dialectical trees as those used traditionally in defeasible argumentation.