What is philosophy of science? Numerous manuals, anthologies, and essays provide carefully reconstructed vantage points on the discipline that have been gained through expert and piecemeal historical analyses. In this article, we address the question from a complementary perspective: we target the content of one major journal in the field-Philosophy of Science-and apply unsupervised text-mining methods to its complete corpus, from its start in 1934 until 2015. By running topic-modeling algorithms over the full-text corpus, we identified 126 key research topics that span 82 years. We also tracked those topics' evolution and fluctuating significance over time in the journal articles. Our results concur with and document known and lesser-known episodes in the philosophy of science, including the rise and fall of logic and language-related topics, the relative stability of a metaphysical and ontological questioning (space and time, causation, natural kinds, realism), the significance of epistemological issues about the nature of scientific knowledge, and the rise of a recent philosophy of biology and other trends. These analyses exemplify how computational text-mining methods can be used to provide an empirical large-scale and data-driven perspective on the history of philosophy of science that is complementary to other current historical approaches.

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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.

La sémiotique computationnelle étudie l’interaction entre les processus d’émergence du sens et les systèmes formels, computables et numériques. En effet, l’une de ses hypothèses est la possibilité de décrire la sémiose à travers des métalangages formels et de la simuler par des procédés algorithmiques. Dans ce contexte, plusieurs pratiques d’analyse sémiotique se sont développées, à l’exemple de l’analyse de texte assistée par ordinateur (ATO). Avec cette dernière, en adoptant des techniques et des méthodes issues de l’informatique et de l’intelligence artificielle, les formes plus classiques de l’analyse de texte se joignent aux champs de recherche des humanités numériques. La sémiotique est ainsi appelée, entre autres, à discuter les enjeux de l’usage de ces techniques dans la recherche en sciences humaines et sociales. L’objectif de cet article est de présenter un survol de la sémiotique computationnelle et d’introduire le lectorat à certains aspects théoriques et méthodologiques de l’assistance informatique à l’analyse de texte. Plus particulièrement, le texte expose les étapes et les hypothèses de la transformation vectorielle du texte que présuppose l’ATO et discute des enjeux sémiotiques de deux procédures : la lemmatisation et la fonction de pondération.