BioEmoDetector: A flexible platform for detecting emotions from health narratives

Abstract

Emotion detection can play a pivotal role in healthcare. Many psychological/psychical illnesses and disorders such as depression, anxiety, or emotional crises can be explained through the study of different emotional changes suffered by a user. One of the mechanisms to detect these emotional changes is the analysis of the user’s expressions/conversations, which can be easily represented as texts. Therefore, it is necessary to count on tools able to recognize and measure the intensity of these emotions. In this sense, at the present moment biomedical and clinical pre-trained language models have been extensively used for text classification tasks; nevertheless, their applications to the field of emotion classification, specially in healthcare, remain relatively unexplored. For that reason, this paper presents BioEmoDetector, an open-source framework for emotion prediction from texts related to medical environments. This tool introduces a flexible framework leveraging multiple biomedical and clinical pre-trained language models which can work individually or together under an ensemble model. This approach provides a powerful tool for understanding the patients’ experiences through their conversations and their impact on health outcomes.

Keywords

• Transfer learning
• Emotion detection
• Clinical and biomedical pre-trained model
• Patient feedback