Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment Analysis

Mauro Dragoni 1 Andrea G. B. Tettamanzi 2 Célia Da Costa Pereira 3
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
3 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MinD
Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : An emerging field within sentiment analysis concerns the investigation about how sentiment polarities associated with concepts have to be adapted with respect to the different domains in which they are used. In this paper, we explore the use of fuzzy logic for modeling concept polarities, and the uncertainty associated with them, with respect to different domains. The approach is based on the use of a knowledge graph built by combining two linguistic resources, namely WordNet and SenticNet. Such a knowledge graph is then exploited by a graph-propagation algorithm that propagates sentiment information learned from labeled datasets. The system implementing the proposed approach has been evaluated on the Blitzer dataset. The results demonstrate its viability in real-world cases.
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Mauro Dragoni, Andrea G. B. Tettamanzi, Célia Da Costa Pereira. Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment Analysis. Cognitive Computation, Springer, 2015, 7 (2), pp.186-197. ⟨10.1007/s12559-014-9308-6⟩. ⟨hal-01150556⟩

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