FFO-ABC DepressioGuard: A Hybrid Classification Framework for Social Media Depression Detection

Abstract

In the phase of digital communication, social media sites have developed into a major centre for individuals to express their ideas, emotions, and views. Amidst this influx of user-generated content, mental health conditions, notably depression, have garnered increasing attention due to their pervasive impact on individuals and societies. Early detection and intervention are crucial in managing and preventing its adverse effects. As a consequence an innovative machine learning (ML) textual data classification framework is designed to detect depression through social media streams, employing a Firefly-Optimized Support Vector Machine (FFO-SVM) and Artificial Bee Colony (ABC) Optimized SVM classifiers. Initially, data collection and preprocessing are performed, followed by feature extraction using Time Frequency-Inverse Document Frequency (TF-IDF). After extraction of features classification is performed using FFO-SVM and ABC-SVM classifiers. To tune the parameters of SVM to work efficiently, FFO and ABC are employed. The proposed framework combines the power of ML with the optimization capabilities of the ABC and FFO Algorithm to enhance classification accuracy. Through extensive experimentation and analysis, the framework's performance is evaluated using relevant metrics. Results indicated that proposed classification techniques outperformed conventional methods, showcasing its effectiveness in handling the complexity of depression detection from social media data.