Producción CyT
Artículo
Autoría
Grosse, Kathrin
;
González, María Paula
;
CHESÑEVAR, CARLOS IVAN
;
MAGUITMAN, ANA GABRIELA
Fecha
2015
Editorial y Lugar de Edición
IOS Press
Revista
AI COMMUNICATIONS,
vol. 28
(pp. 387-401)
IOS Press
Resumen
Información suministrada por el agente en
SIGEVA
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 infor...
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.
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Palabras Clave
ArgumentationArtificial IntelligenceSocial MediaOpinion Mining
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