Identifying suicidal ideation in texts using semi-supervised learning
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1321Keywords:
Emotion Analysis, Suicidal Ideation, Mental HealthAbstract
Objective: To improve the Boamente model using semi-supervised learning methods for the identification of suicidal ideation in non-clinical texts written in Brazilian Portuguese, in order to improve its performance. Method: New data was collected and different semi-supervised learning methods with an emphasis on emotion analysis were applied to improve the existing model. Results: The results showed an improvement of between 2.39% and 4.30% in the accuracy metric compared to the original model, with the self-learning method achieving the best performance. Conclusion: The application of semi-supervised learning methods improved the performance of the Boamente model for identifying suicidal ideation. This study therefore contributes to the development of a more effective tool for mental health professionals in suicide prevention, helping them to make more assertive decisions when monitoring their patients.
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