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Journal of the International Association for Semiotic Studies / Revue de l'Association Internationale de Sémiotique

Editor-in-Chief: Danesi, Marcel

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Volume 2019, Issue 230


A data-driven computational semiotics: The semantic vector space of Magritte’s artworks

Jean-François Chartier
  • Corresponding author
  • Centre interuniversitaire de recherche sur la science et la technologie, Université du Québec à Montréal, C.P. 8888, succ. Centre-ville, Montreal, Quebec H3C 3P8, Canada
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  • Laboratoire D’ANalyse Cognitive de l’Information, Informatique, Universite du Quebec a Montreal, 201, avenue du Président-Kennedy, Local PK-4150, Montreal, Quebec H2X 3Y7, Canada
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Published Online: 2019-09-05 | DOI: https://doi.org/10.1515/sem-2018-0120


The rise of big digital data is changing the framework within which linguists, sociologists, anthropologists, and other researchers are working. Semiotics is not spared by this paradigm shift. A data-driven computational semiotics is the study with an intensive use of computational methods of patterns in human-created contents related to semiotic phenomena. One of the most promising frameworks in this research program is the Semantic Vector Space (SVS) models and their methods. The objective of this article is to contribute to the exploration of the SVS for a computational semiotics by showing what types of semiotic analysis can be accomplished within this framework. The study is applied to a unique body of digitized artworks. We conducted three short experiments in which we explore three types of semiotic analysis: paradigmatic analysis, componential analysis, and topic modelling analysis. The results reported show that the SVS constitutes a powerful framework within which various types of semiotic analysis can be carried out.

Keywords: computational semiotics; semantic vector space; data-driven; paradigmatic analysis; component analysis; topic analysis; artwork mining; René Magritte


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Published Online: 2019-09-05

Published in Print: 2019-10-25

Citation Information: Semiotica, Volume 2019, Issue 230, Pages 19–69, ISSN (Online) 1613-3692, ISSN (Print) 0037-1998, DOI: https://doi.org/10.1515/sem-2018-0120.

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