Abstract:
This study investigates the potential of machine learning algorithms in detecting signs of
depression within Bengali text on social media platforms. As mental health concerns continue to
rise globally, understanding linguistic patterns indicative of depression in the unique context of
the Bengali-speaking population becomes imperative. Leveraging natural language processing, the
study employs a variety of machine learning algorithms, including Support Vector Machine
(SVM), Random Forest, Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term
Memory (Bi-LSTM). Ethical considerations take precedence throughout the study, focusing on
user privacy, informed consent, and cultural sensitivity. The research aims not only to develop
effective depression detection models but also to ensure responsible data governance and user
empowerment. By fostering a supportive environment for mental health discussions, the study
aligns its objectives with ethical principles. Results showcase promising accuracy rates, with SVM
leading at 86%, followed by LSTM at 83%, Bi-LSTM at 81%, and Random Forest at 79%. Beyond
accuracy, the study evaluates precision, recall, and F1-score metrics to provide a comprehensive
understanding of each algorithm's performance. The implications of the research extend beyond
numerical metrics. The study advocates for the development of culturally sensitive and user-centric
interventions, emphasizing the importance of ethical considerations in deploying artificial
intelligence for mental health support. In the Bengali-speaking community, where cultural nuances
play a significant role in linguistic expressions, the study's outcomes contribute valuable insights
for tailoring depression detection tools to local contexts.
