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

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Semantic Web Challenges (SemWebEval 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 641))

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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.

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Notes

  1. 1.

    http://sentic.net/.

  2. 2.

    http://commons.media.mit.edu/en/.

  3. 3.

    http://www.wjh.harvard.edu/~inquirer/spreadsheet_guide.htm.

  4. 4.

    All the material used for the evaluation and the built models are available at http://goo.gl/pj0nWS.

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Author information

Authors and Affiliations

  1. Universitá di Trento, Trento, Italy

    Giulio Petrucci

  2. Fondazione Bruno Kessler, Trento, Italy

    Mauro Dragoni

Authors
  1. Giulio Petrucci
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  2. Mauro Dragoni
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Correspondence to Mauro Dragoni .

Editor information

Editors and Affiliations

  1. IT Systems Engineering, Hasso-Plattner Institute, Potsdam, Germany

    Harald Sack

  2. Leibniz Universität Hannover , Hannover, Germany

    Stefan Dietze

  3. Elsevier B.V. , Amsterdem, The Netherlands

    Anna Tordai

  4. Universität Bonn , Bonn, Germany

    Christoph Lange

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Petrucci, G., Dragoni, M. (2016). The IRMUDOSA System at ESWC-2016 Challenge on Semantic Sentiment Analysis. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds) Semantic Web Challenges. SemWebEval 2016. Communications in Computer and Information Science, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-46565-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-46565-4_10

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