Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment Analysis

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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Notes

  1. 1.

    https://wordnet.princeton.edu/.

  2. 2.

    http://sentic.net/.

  3. 3.

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

  4. 4.

    http://www.cs.jhu.edu/~mdredze/datasets/sentiment/.

  5. 5.

    http://www.cs.jhu.edu/~mdredze/datasets/sentiment/.

  6. 6.

    Detailed results and tool demo are available at http://dkmtools.fbk.eu/moki/demo/mdfsa/mdfsa_demo.html.

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Correspondence to Mauro Dragoni.

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Dragoni, M., Tettamanzi, A.G.B. & da Costa Pereira, C. Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment Analysis. Cogn Comput 7, 186–197 (2015). https://doi.org/10.1007/s12559-014-9308-6

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Keywords

  • Sentiment analysis
  • Multi-domain learning
  • Fuzzy logic