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Challenge on Fine-Grained Sentiment Analysis Within ESWC2016

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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

The wide spread of the social media has given users a means to express and share their opinions and thoughts on a large range of topics and events. The number of opinions, emotions, sentiments that are being expressed within social media grows at an exponential rate; all these data can be exploited in order to come up with useful insights, analytics, etc. Initial Sentiment Analysis systems used lexical and statistical resources to automatically assess polarities of opinions and sentiment. With the raise of the Semantic Web, it has been proved that Sentiment Analysis techniques can have higher performances if they use semantic features. This generated further opportunities for the research domain as well as the market domain where key stakeholders need to catch up with the latest technology if they want to be compelling. Therefore, deep understanding of natural language text and the related semantics are urgent matter to be familiar with. Following the first two editions, the third edition of the Fine-Grained Sentiment Analysis challenge aims at providing a stimulus toward this direction. On the one hand, it represents an event where researchers can learn and share their methods and how they employed Semantics for Sentiment Analysis. On the other hand, it offers an occasion for stakeholders to get an idea of what research is being developed and where the research is headed to plan future strategies within the domain of Sentiment Analysis.

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Notes

  1. 1.

    https://github.com/diegoref/SSA2016.

  2. 2.

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

  3. 3.

    http://dbpedia.org.

  4. 4.

    Publicly accessible at https://groups.google.com/forum/#!forum/semantic-sentiment-analysis.

  5. 5.

    http://www.maurodragoni.com/research/opinionmining/events/.

  6. 6.

    http://2016.eswc-conferences.org/.

  7. 7.

    https://en.wikipedia.org/wiki/SemEval.

  8. 8.

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

  9. 9.

    http://naacl.org/naacl-hlt-2016/index.html.

  10. 10.

    http://sentic.net/.

  11. 11.

    http://sentic.net/sentire/.

  12. 12.

    http://sentic.net/wisdom/.

  13. 13.

    http://sentimentsymposium.com.

  14. 14.

    http://challenge.semanticweb.org/.

  15. 15.

    http://iswc2016.semanticweb.org/.

  16. 16.

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

  17. 17.

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

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Acknowledgement

Challenge Organizers want to thank Springer for supporting the provided awards also for this year edition. Moreover, the research leading to these results has received funding from the European Union Horizons 2020 the Framework Programme for Research and Innovation (2014–2020) under grant agreement 643808 Project MARIO Managing active and healthy aging with use of caring service robots.

Author information

Authors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Mauro Dragoni

  2. Universitá di Cagliari, Cagliari, Italy

    Diego Reforgiato Recupero

Authors
  1. Mauro Dragoni
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  2. Diego Reforgiato Recupero
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Corresponding author

Correspondence to Diego Reforgiato Recupero .

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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Dragoni, M., Reforgiato Recupero, D. (2016). Challenge on Fine-Grained Sentiment Analysis Within ESWC2016. 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_6

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  • DOI: https://doi.org/10.1007/978-3-319-46565-4_6

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