A Deep Learning Based Approach for Depression Detection from Social Media Text

It can be concluded that the growing prevalence of depression and the widespread use of social media have created an urgent need for intelligent and automated depression detection systems. Machine Learning and Deep Learning models offer significant advantages in extracting meaningful patterns from large volumes of social media data, enabling early diagnosis and continuous mental health monitoring. Although considerable progress has been achieved through advanced neural architectures and transformer-based models, challenges related to data quality, privacy, interpretability, and model generalization remain unresolved. Addressing these issues will pave the way for more reliable and ethically responsible systems that can assist healthcare professionals and contribute to improved mental health outcomes. The deep learning model presented in this work is shown to attain improved classification accuracy compared to existing work in the domain of research.

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