| Resumen |
This paper presents MF-SET, a multi-task framework for sentiment analysis and aspect-opinion extraction in student evaluations of teaching (SET) in Spanish, which is an understudied area. We first prepared a novel aspect-opinion extraction dataset in Spanish that evaluates nine different aspects of teaching in both positive and negative light. We then developed a multi-task learning framework, including opinion segmentation, multi-class classification, and multi-label classification models for extracting information from Spanish text. The framework uses text generative abilities of GPT-3 combined with BERT to deal with the task. For multi-class classification, we used BERT with different feature sets and sentiments about teachers with respect to their recommendation, intellectual challenges, and learning guidance. Our results show that the model’s performance varies depending on the feature sets and classifiers. Opinion segmentation and multi-label classification were fine-tuned on GPT-3 for the best results of 0.68 Micro F1 for positive and 0.72 for negative aspects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
