Recommender System for Postpartum Depression Monitoring based on Sentiment Analysis

Autor: Erica L. Gallindo, Joel J. P. C. Rodrigues, Marcilio B. Carneiro, Silas S. L. Pereira, Mario W. L. Moreira
Rok vydání: 2021
Předmět:
Zdroj: HealthCom
DOI: 10.1109/healthcom49281.2021.9398922
Popis: Emotions influence all aspects of human behavior. All of these aspects shape people's lives, directly impacting their ways of life. Some diseases are directly linked to emotions. Among them, depression is one of the diseases with the greatest impact on society. Hence, faced with this problem, the objective of this study is to present a context-aware solution based on text mining for gestational depression prevention. This system uses text mining to analyze documents filled from pregnant women in order to identify their feelings through natural language processing techniques and probabilistic algorithms. As a case study, the analyzed texts were obtained from forms answered by pregnant women. The model performance is evaluated using metrics associated with the confusion matrix. The results show that the proposed model has achieved a reliable performance in all metrics, mainly when classifying new cases. Thus, the results obtained by the model can be used as support to health professionals in monitoring high-risk pregnancies.
Databáze: OpenAIRE