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Semantic Web Evaluation Challenges

SemWebEval 2015: Semantic Web Evaluation Challenges pp 211–222Cite as

ESWC 15 Challenge on Concept-Level Sentiment Analysis

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 548))

Abstract

A consequence of the massive use of social networks, blogs, wikis, etc., is the change of users’ behaviour on, and their interaction with, the Web: opinions, emotions and sentiments are now expressed differently from the past. Lexical understanding of text is not anymore enough to detect sentiment polarities. Semantics became key for sentiment detection. This generates potential business opportunities, especially within the marketing area, and key stakeholders need to catch up with the latest technology if they want to be compelling in the market. Therefore, understanding the opinions and its peculiarities from a written text involves a deep understanding of natural language text and the semantics behind it. Recently, it has been proved that the use of semantics improves the accuracy of existing sentiment analysis systems, which are mainly based on pure machine learning or other statistical approaches. The second Edition of the Concept Level Sentiment Analysis challenge aims to provide a further stimulus in this direction by offering to researchers an event where they can learn and experiment on how to employ Semantic Web features within their sentiment analysis systems, aiming at reaching higher performance.

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Notes

  1. 1.

    http://2015.eswc-conferences.org/important-dates/call-CLSA.

  2. 2.

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

  3. 3.

    https://github.com/diegoref/ESWC-CLSA.

  4. 4.

    http://dbpedia.org.

  5. 5.

    Chapter on Concept Level Sentiment Analysis.

  6. 6.

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

  7. 7.

    http://sentic.net/.

  8. 8.

    http://sentic.net/sentire/.

  9. 9.

    http://sentic.net/wisdom/.

  10. 10.

    http://challenge.semanticweb.org/2014/submissions/.

  11. 11.

    http://sentic.net/sentics/.

  12. 12.

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

  13. 13.

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

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Acknowledgement

Challenge Organisers want to thank Springer for supporting the provided awards.

Author information

Authors and Affiliations

  1. Catania, Italy

    Diego Reforgiato Recupero, Mauro Dragoni & Valentina Presutti

Authors
  1. Diego Reforgiato Recupero
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  2. Mauro Dragoni
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  3. Valentina Presutti
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Corresponding author

Correspondence to Diego Reforgiato Recupero .

Editor information

Editors and Affiliations

  1. Inria, Sophia Antipolis, France

    Fabien Gandon

  2. INRIA Sophia-Antipolis Méditerranée, Sophia Antipolis, France

    Elena Cabrio

  3. Université Paris-Sorbonne, Paris, France

    Milan Stankovic

  4. École des Mines de Saint-Étienne, Saint-Étienne, France

    Antoine Zimmermann

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Recupero, D.R., Dragoni, M., Presutti, V. (2015). ESWC 15 Challenge on Concept-Level Sentiment Analysis. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds) Semantic Web Evaluation Challenges. SemWebEval 2015. Communications in Computer and Information Science, vol 548. Springer, Cham. https://doi.org/10.1007/978-3-319-25518-7_18

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  • DOI: https://doi.org/10.1007/978-3-319-25518-7_18

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