Insight into computational techniques in detecting depression in text
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1363Keywords:
Computing, Depression, RevisionAbstract
Objective: Review the literature on depression detection in texts, focusing on machine learning and natural language processing techniques.
Method: Analysis of studies using advanced computational techniques and dictionaries of depression-indicative words, considering the integration of machine learning methods, natural language processing, and mental health resources.
Results: Most works use advanced computational techniques and specific dictionaries, but there is little integration of linguistics and mental health in the models. A gap in incorporating the cultural and regional context of written language was observed.
Conclusion: Emphasizes the need to include linguistics to consider the cultural and regional context and increase the use of mental health resources in identifying depression in texts, improving the precision and effectiveness of detection tools.
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