A TEXT MINING CLASSIFICATION OF ARTICLES ON SOCIAL SUSTAINABILITY IN SUPPLY CHAINS

Abstract

The purpose of this paper is to categorize articles on social sustainability in supply chains based on the methodology used to address the topic. By reading all articles one by one, the paper considers the use of text mining as an alternative to manual classification. This work reduces the time it takes to find information in a body of literature from months to minutes. Practitioners should ensure that the data language used to describe the topic is correct and complete. The findings of the paper assist researchers and postgraduate students in completing time-consuming tasks such as literature reviews. The current work could be improved by incorporating contextual information classification rather than frequency classification.