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Semantic Web Evaluation Challenge

SemWebEval 2017: Semantic Web Challenges pp 133–147Cite as

Aspect-Based Opinion Mining Using Knowledge Bases

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 769))

Abstract

In the last decade, the focus of the Opinion Mining field moved to detection of the pairs “aspect-polarity” instead of limiting approaches in the computation of the general polarity of a text. In this work, we propose an aspect-based opinion mining system based on the use of semantic resources for the extraction of the aspects from a text and for the computation of their polarities. The proposed system participated at the third edition of the Semantic Sentiment Analysis (SSA) challenge took place during ESWC 2017 achieving the runner-up place in the Task #2 concerning the aspect-based sentiment analysis. Moreover, a further evaluation performed on the SemEval 2015 benchmarks demonstrated the feasibility of the proposed approach.

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Notes

  1. 1.

    http://alt.qcri.org/semeval2015/cdrom/pdf/SemEval082.pdf.

  2. 2.

    https://wordnet.princeton.edu/.

  3. 3.

    http://sentic.net/.

  4. 4.

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

  5. 5.

    The used stopwords list is available at http://www.lextek.com/manuals/onix/stopwords1.html.

  6. 6.

    http://stanfordnlp.github.io/CoreNLP/index.html.

  7. 7.

    http://www.alt.qcri.org/semeval2015/task12/.

  8. 8.

    http://alt.qcri.org/semeval2016/task5/.

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Author information

Authors and Affiliations

  1. Universitá di Trento, Trento, Italy

    Marco Federici

  2. Fondazione Bruno Kessler, Trento, Italy

    Mauro Dragoni

Authors
  1. Marco Federici
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  2. Mauro Dragoni
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Correspondence to Mauro Dragoni .

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Editors and Affiliations

  1. Fondazione Bruno Kessler , Povo, Trento, Italy

    Mauro Dragoni

  2. University of Oxford , Oxford, United Kingdom

    Monika Solanki

  3. Linköping University , Linköping, Sweden

    Eva Blomqvist

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Federici, M., Dragoni, M. (2017). Aspect-Based Opinion Mining Using Knowledge Bases. In: Dragoni, M., Solanki, M., Blomqvist, E. (eds) Semantic Web Challenges. SemWebEval 2017. Communications in Computer and Information Science, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-69146-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-69146-6_13

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