Support for a Chat-Based Intervention to Reduce Alcohol Consumption via Emotion Detection - Barbosa, Luan Henrique da S.; Bernardino, Heder S. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.21291, 2026)
Harmful alcohol consumption constitutes a public health problem associated with social and health impacts. Chat-based interventions conducted by psychologists emerge as an alternative to reduce the harm caused by alcohol, and text emotion detection can support professionals with additional information. It is expected that identifying emotions will allow consultants to monitor the patient’s emotional state, facilitating more sensitive interventions. Thus, this work evaluates three datasets and five emotion classification models, aiming to provide a basis for the implementation of the service. The TweetEmotions, PlayStoreReviews, and GoEmotions datasets were analyzed, along with Logistic Regression, LinearSVC, Random Forest, XGBoost, and an adjusted BERTimbaubase model, as well as different preprocessing, balancing, and feature engineering strategies. The GoEmotions dataset proved to be the most suitable for the proposed scenario due to its multi-label characteristic. The results indicate consistent performance, with a macro F1-score of 0.40, making it possible to offer professionals an additional resource to support decision-making and contribute to better-targeted care and improved quality of support.