<![CDATA[This research aims to develop an optimized hybrid deep learning model for detecting suicidal ideation from social media text. The growing volume of online discussions, particularly on platforms such as Reddit, provides valuable signals for early identification of individuals at risk; however, the linguistic characteristics of user-generated content are highly diverse and often noisy. To address this challenge, this study proposes an Optimized CNN-BiLSTM architecture enhanced with a dropout rate of 0.6 and a strategic training approach utilizing Early Stopping (patience=3) and a Learning Rate Scheduler (ReduceLROnPlateau) to prevent local minima and ensure convergence stability. The dataset used consists of 232,074 text entries with a balanced class distribution (50% suicide, 50% non-suicide) to ensure the validity of evaluation metrics and eliminate majority class bias. Experimental results demonstrate that the optimized model achieves an accuracy of 94.96%, precision of 95.70%, recall of 94.15%, and an F1-score of 94.92%, indicating a significant improvement over the baseline CNN-BiLSTM and single BiLSTM models. Furthermore, interpretability analysis via keyword visualization (Word Cloud) validates that the model effectively captures semantically relevant emotional expressions of despair. These findings suggest that the optimized hybrid architecture provides a robust and operationally viable approach for supporting real-time early-warning systems on social media platforms to facilitate timely mental health interventions.]]>
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