2019-12-14T21:38:22Z
http://digital.csic.es/dspace-oai/request
oai:digital.csic.es:10261/163022
2018-08-09T10:11:11Z
com_10261_60
com_10261_4
col_10261_439
Alsinet, Teresa
Argelich, Josep
Bejar, Ramon
Esteva, Francesc
Godo, Lluis
2018-03-28T12:03:07Z
2018-03-28T12:03:07Z
2017-08-20
IJCAI-17 Workshop on Logical Foundations for Uncertainty and Machine Learning (LFU-2017): 3- 8 (2017)
http://hdl.handle.net/10261/163022
http://dx.doi.org/10.13039/501100004837
In a recent work some of the authors have developed an argumentative approach for discovering relevant opinions in Twitter discussions with probabilistic valued relationships. Given a Twitter discussion, the system builds an argument graph where each node denotes a tweet and each edge denotes a criticism relationship between a pair of tweets of the discussion. Relationships between tweets are associated with a probability value, indicating the uncertainty that the relationships hold. In this work we introduce and investigate a natural extension of the representation model, referred as probabilistic author-centered model, in which tweets within a discussion are grouped by authors, in such a way that tweets of a same author describe his/her opinion in the discussion and are rep- resented with a single node in the graph, and criticism relationships denote controversies between opinions of Twitter users in the discussion. In this new model, the interactions between authors can give rise to circular criticism relationships, and the probability of one opinion criticizing another has to be evaluated from the probabilities of criticism among the tweets that compose both opinions.
eng
closedAccess
Author-centered model
Argument graph
Twitter discussions
A probabilistic author-centered model for Twitter discussions
comunicaciĆ³n de congreso