A Comparison of Multi-Label Text Classification Models in Research Articles Labeled With Sustainable Development Goals

Abstract

The classi cation of scienti c articles aligned to Sustainable Development Goals is crucial for research institutions and universities when assessing their in uence in these areas. Machine learning enables the implementation of massive text data classi cation tasks. The objective of this study is to apply Natural Language Processing techniques to articles from peer-reviewed journals to facilitate their classi cation according to the 17 Sustainable Development Goals of the 2030 Agenda. This article compares the performance of multi-label text classi cation models based on a proposed framework with datasets of different characteristics. The results show that the combination of Label Powerset (a transformation method) with Support Vector Machine (a classi cation algorithm) can achieve an accuracy of up to 87% for an imbalanced dataset, 83% for a dataset with the same number of instances per label, and even 91% for a multiclass dataset.