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

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Abstract

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.

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Availability of supporting data

The lexicons are freely available on Dataverse (Deléglise et al., 2021b). The extracted newspaper corpus is available in restricted access for legal reasons on Dataverse (Deléglise et al., 2021a).

Notes

  1. https://www.openstreetmap.fr/

  2. http://www.geonames.org/

  3. http://www.lobservateur.bf/

  4. https://lefaso.net/

  5. https://www.sidwaya.info/

  6. https://www.journaldujeudi.com/

  7. https://www.burkina24.com/

  8. http://www.fasozine.com/

  9. http://cultivoo.fr/index.php/developpement-durable/agriculture/2590-vocabulaire-sur-la-securite-alimentaire

  10. https://pypi.org/project/vaderSentiment-fr/

  11. https://github.com/pipapou/Burkina_localites

  12. https://github.com/pipapou/analyse_corpus

  13. https://spacy.io/api/lemmatizer

  14. https://rsf.org/fr/methodologie-detaillee-du-classement-mondial-de-la-liberte-de-la-presse

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Acknowledgements

This study was conducted with the help of the Ministry of Agriculture, Water Resources, Sanitation and Food Security of Burkina Faso, the World Food Program (WFP) and the Société Nationale de Gestion du Stock de Sécurité Alimentaire (SONAGESS), which provided data.

Funding

This work was supported by the French National Research Agency under the Investments for the Future Program #DigitAg, referred to as ANR-16-CONV-0004.

Author information

Authors and Affiliations

  1. TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France

    Hugo Deléglise, Agnès Bégué, Roberto Interdonato, Mathieu Roche & Maguelonne Teisseire

  2. CIRAD, UMR TETIS, Montpellier, F-34398, France

    Hugo Deléglise, Agnès Bégué, Roberto Interdonato & Mathieu Roche

  3. MOISA, Univ Montpellier, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Montpellier, France

    Elodie Maître d’Hôtel

  4. CIRAD, UMR MOISA, Montpellier, F-34398, France

    Elodie Maître d’Hôtel

  5. INRAE, Montpellier, France

    Maguelonne Teisseire

Authors
  1. Hugo Deléglise
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  2. Agnès Bégué
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  3. Roberto Interdonato
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  4. Elodie Maître d’Hôtel
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  5. Mathieu Roche
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  6. Maguelonne Teisseire
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Contributions

HD, RI, MR and MT conceived the work and wrote the main manuscript text, HD processed the data and performed the experiments, AB and EMH contributed to the discussion of obtained results. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Hugo Deléglise or Roberto Interdonato.

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Deléglise, H., Bégué, A., Interdonato, R. et al. How can text mining improve the explainability of Food security situations?. J Intell Inf Syst (2023). https://doi.org/10.1007/s10844-023-00832-x

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  • DOI: https://doi.org/10.1007/s10844-023-00832-x

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