Classifying, Representing and Labelling: Methodological Framework and Associated Tools for an Automatic Emergence of Themes on Textual Data

Classifier, représenter et labelliser: Cadre et outils méthodologiques associés pour une émergence automatique de thématiques sur données textuelles

Sarah Maire () and Sébastien Liarte ()
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Sarah Maire: CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine
Sébastien Liarte: BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique

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Abstract: This paper aims to propose a methodological framework allowing the automatic emergence of themes from textual data. This framework is composed of both a three steps process – classification, representation and labelling – and specific tools, related to statistical technics and software. This method is enrolled in the field of textmining, which is developing now. The purpose is also to present some specific tools, related to statistical technics and software. Finally, this approach is applied to the text in the totality of Scouts and Guides of France annual calendars, from 1936 to 2016. We made emerged three main themes: community, organisation and religion. These three themes could be assimilated to institutional logics, which are identified a posteriori from a textual corpus and without the observation of a priori criterions.

Keywords: Methodology; Textual search; Qualitative data; Quantitative methods; Méthodologie; Fouille textuelle; Données qualitatives; Méthodes quantitatives (search for similar items in EconPapers)
Date: 2019-05-23
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Published in Finance Contrôle Stratégie, 2019, NS-6, pp.1-21. ⟨10.4000/fcs.3423⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02518168

DOI: 10.4000/fcs.3423

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