A machine learning approach for analyzing Free JAR data

Alexiane Luc (1) , Sébastien Lê (2) , Mathilde Philippe (3) , El Mostafa Qannari (1) , Evelyne Vigneau (1)

1 StatSC - Statistique, Sensométrie et Chimiométrie
2 IRMAR - Institut de Recherche Mathématique de Rennes
3 Strategir - R&D and Image & Technology departments

This paper presents a methodology based on a machine learning approach for the processing of Free JAR data. The development of this new analysis method is grounded on the fact that Free JAR data can be seen as labeled textual data, since a hedonic category is associated to each Free JAR comment. In particular, this two-step methodology aims to take advantage of the link between these two pieces of information, to disclose the products' assets and weaknesses. The first step consists of assigning valency scores to the Free JAR comments, considering their link to the hedonic categorization provided by the consumers. This is achieved by setting up a classifier based on Random Forest. The second step consists of highlighting the attributes that characterize the product under study based on the concept of interpretability. The interest of the proposed procedure is illustrated through a case study pertaining to cheeses.

Mots clés: Free JAR; Labeled textual data; Valency score; Interpretability; Preference mapping; Cheese

Domaines: Ingénierie des aliments; Machine Learning [stat.ML]

Alexiane Luc, Sébastien Lê, Mathilde Philippe, El Mostafa Qannari, Evelyne Vigneau. A machine learning approach for analyzing Free JAR data. Food Quality and Preference, 2022, 99, pp.article n° 104581. ⟨10.1016/j.foodqual.2022.104581⟩. ⟨hal-03675589⟩