Bi-LSTM-Based Sentiment Analysis for Identifying Depression Indicators in Twitter Posts

Depression remains a significant global mental health issue, and early detection is crucial for timely intervention.Traditional methods often struggle to identify subtle indicators of depression, especially in social media content.This research introduces a novel methodology that combines sentiment analysis with a Bi-directional Long Short-Term Memory (Bi-LSTM) model to identify linguistic markers of depression in social media posts, specifically on Twitter.Sentiment analysis captures emotional cues from text, while the Bi-LSTM model effectively recognizes complex linguistic patterns, enabling a more nuanced detection of depression.The proposed approach overcomes limitations of conventional machine learning models by integrating these techniques, which enhances the identification of both emotional sentiment and linguistic features associated with depressive tendencies.The dataset used for this study is the Kaggle: Depression on Twitter, containing labeled Twitter posts related to depression.The model's performance is evaluated through key metrics, achieving an accuracy of 98.65%, precision of 99.13%, recall of 98.09%, and an F1 score of 98.6%.These results demonstrate the potential of deep learning techniques to improve depression detection in social media content, offering a more reliable and efficient tool for early intervention.This research contributes to the growing field of mental health analysis using social media data, providing a robust framework that can be integrated into mental health support systems to aid in reducing the global burden of depression.