Article Dans Une Revue Journal of Intelligent Information Systems Année : 2024

How can text mining improve the explainability of Food security situations?

Résumé

Food Security (FS) is a major concern in West Africa, particularly in Burkina Faso, which has been the epicenter of a humanitarian crisis since the beginning of this century. Early warning systems for FS and famines rely mainly on numerical data for their analyses, whereas textual data, which are more complex to process, are rarely used. However, this data is easy to access and represents a source of relevant information that is complementary to commonly used data sources. This study explores methods for obtaining the explanatory context associated with FS from textual data. Based on a corpus of local newspaper articles, we analyze FS over the last ten years in Burkina Faso. We propose an original and dedicated pipeline that combines different textual analysis approaches to obtain an explanatory model evaluated on real-world and large-scale data. The results of our analyses have proven how our approach provides significant results that offer distinct and complementary qualitative information on food security and its spatial and temporal characteristics.
Fichier non déposé

Dates et versions

hal-04573414 , version 1 (13-05-2024)

Identifiants

Citer

Hugo Deléglise, Agnès Bégué, Roberto Interdonato, Elodie Maître D’hôtel, Mathieu Roche, et al.. How can text mining improve the explainability of Food security situations?. Journal of Intelligent Information Systems, 2024, 62 (4), pp.971-994. ⟨10.1007/s10844-023-00832-x⟩. ⟨hal-04573414⟩
65 Consultations
0 Téléchargements

Altmetric

Partager

More