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The IRMUDOSA System at ESWC-2016 Challenge on Semantic Sentiment Analysis

  • Giulio Petrucci
  • Mauro Dragoni
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 641)

Abstract

Multi-domain opinion mining consists in estimating the polarity of a document by exploiting domain-specific information. One of the main issue of the approaches discussed in literature is their poor capability of being applied on domains that have not been used for building the opinion model. In this paper, we present an approach exploiting the linguistic overlap between domains for building models enabling the estimation of polarities for documents belonging to any other domain. The system implementing such an approach has been presented at the third edition of the Semantic Sentiment Analysis Challenge co-located with ESWC 2016. Fuzzy representation of features polarity supports the modeling of information uncertainty learned from training set and integrated with knowledge extracted from two well-known resources used in the opinion mining field, namely Sentic.Net and the General Inquirer. The proposed technique has been validated on a multi-domain dataset and the results demonstrated the effectiveness of the proposed approach by setting a plausible starting point for future work.

Keywords

Opinion Mining Opinion Classification Fuzzy Membership Function Opinion Word General Inquirer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Universitá di TrentoTrentoItaly
  2. 2.Fondazione Bruno KesslerTrentoItaly

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