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Semantic Web-Based Social Media Analysis

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Transactions on Computational Collective Intelligence XXII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 9655))

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Abstract

With the on growing usage of microblogging services, such as Twitter, millions of users share opinions daily on virtually everything. Making sense of this huge amount of data using sentiment and emotion analysis, can provide invaluable benefits to organizations trying to better understand what the public thinks about their services and products. While the vast majority of now-a-days researches are solely focusing on improving the algorithms used for sentiment and emotion evaluation, the present one underlines the benefits of using a semantic based approach for modeling the analysis’ results, the emotions and the social media specific concepts. By storing the results as structured data, the possibilities offered by semantic web technologies, such as inference and accessing the vast knowledge in Linked Open Data, can be fully exploited. The paper also presents a novel semantic social media analysis platform, which is able to properly emphasize the users’ complex feeling such as happiness, affection, surprise, anger or sadness.

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Notes

  1. 1.

    http://www.twitter.com.

  2. 2.

    http://linkeddata.org.

  3. 3.

    http://dublincore.org/.

  4. 4.

    http://xmlns.com/foaf/spec/.

  5. 5.

    http://sioc-project.org/.

  6. 6.

    http://www.w3.org/2003/01/geo/.

  7. 7.

    http://cassandra.apache.org/.

  8. 8.

    http://multiwordnet.fbk.eu/.

  9. 9.

    http://www.sentiment140.com/.

  10. 10.

    http://www.netlingo.com/smileys.php.

  11. 11.

    http://www.smileyontology.com/.

  12. 12.

    http://www.sananalytics.com/lab/twitter-sentiment/.

  13. 13.

    http://wiki.dbpedia.org/.

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Acknowledgments

The study was produced as part of the SCOPANUM research project, supported by grants from CSFRS (http://csfrs.fr/), and a doctoral grant from Pays de Montbéliard Agglomération (http://www.agglo-montbeliard.fr/). The authors also acknowledge the support of Leverhulme Trust International Network research project “IN-2014-020”.

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Authors and Affiliations

  1. Franche-Comté University, Montbéliard, France

    Liviu-Adrian Cotfas, Antonin Segault & Ioan Roxin

  2. Bucharest University of Economic Studies, Bucharest, Romania

    Liviu-Adrian Cotfas & Camelia Delcea

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  1. Liviu-Adrian Cotfas
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  2. Camelia Delcea
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  3. Antonin Segault
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Correspondence to Liviu-Adrian Cotfas .

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Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, Australia

    Ryszard Kowalczyk

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Cotfas, LA., Delcea, C., Segault, A., Roxin, I. (2016). Semantic Web-Based Social Media Analysis. In: Nguyen, N.T., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence XXII. Lecture Notes in Computer Science(), vol 9655. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49619-0_8

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  • DOI: https://doi.org/10.1007/978-3-662-49619-0_8

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