Text Mining
analyse du contenu du rapport du commissaire aux comptes
DOI :
https://doi.org/10.17533/udea.rc.n83a06Mots-clés :
Rapport d’audit, Text Mining, commissaire aux comptes, contenu, utilisateurs de l’informationRésumé
Cet article évalue le contenu du rapport d’audit - dans le cas de la Colombie, le rapport du commissaire aux comptes - au cas des entreprises cotées à la bourse colombienne au cours de la période 2010-2021, en utilisant la méthodologie du Text Mining, qui a permis d’analyser le texte inclus dans 647 rapports du commissaire aux comptes, en identifiant les termes les plus récurrents et leur importance dans le rapport, à travers la fréquence statistique des termes et les nuages de mots. Les mots ou termes les plus utilisés dans les rapports sont “financier”, “audit” et “opinion”, mais dans les paragraphes d’informations supplémentaires tels que les paragraphes d’emphase et les questions clés, d’autres sujets tels que les procédures, la dépréciation, le portefeuille, l’évolution et les actifs sont incorporés pour faire référence aux informations supplémentaires de l’évaluation des auditeurs autres que l’émission d’une simple opinion d’audit.
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