• Miss. Anagha Ubale
• Miss. Prajakta Sonawane
• Miss. Pragati Bhandalkar
• Miss. Shrutika Gorgal
• Prof. R. N. Kadam

Depression is a leading cause of mental health disorders, impacting millions worldwide. Early detection and intervention are crucial indicators of improved patient outcomes and may require further research. This study presents a Machine Learning (ML) based Depression Detection System incorporating facial expression analysis, text mining procedures, and voice-based emotion recognition to assess the depression of users. Specifically, the system employs Haar Cascade Classifier for the canonical facial detection, the Xception Convolution Neural Network (CNN) model for emotion recognition, and speech recognition algorithms utilizing voice-based approaches. Depression is an all-too-common mental health challenge that is often masked and impossible to identify by conventional clinical methods. With the widespread use of social media platforms like Twitter, Reddit, Facebook, Instagram, and Weibo; several new methods of detection have emerged which utilize new ML or DL. These approaches provide means of identifying online expressions of behavior and communication that in turn provide enhanced means of accurately assessing individual's symptoms. Over time, many methods have been developed to accomplish this task, but in part due to rapid growth in research, and search systems overlooking use papers, navigating the relevant studies of all the various approaches is difficult, if not overwhelming. Although some review articles do exist, many of them are vague about the evolution of this field, the new developments and methods being developed, or even where the challenges lie. This article attempts to address this gap by providing a comprehensive overview of ML and DL approaches for detecting depression through social media data, offering a general framework for system design, providing a review of commonly- used datasets and methodologies, and identifying areas that require further research. This review focuses on social media communities, and thus informs the reader on how modern technologies can potentially aid in the detection of depression, but its focus does keep this review from including an all-encompassing review of relevant methods such as graph-based or reinforcement learning methods. Therefore, the findings of this article, can potentially be constrained socially, based on the use of depression detection through social media.

Depression detection, hybrid machine learning, Deep learning, machine learning, natural language processing, sentiment analysis, social media, convolution neural network, long short term memory.

"Facial Expression Analysis with Haar Cascade for Automated Detection of Depression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.b536-b539, January-2026, Available :http://www.jetir.org/papers/JETIR2601178.pdf

"Facial Expression Analysis with Haar Cascade for Automated Detection of Depression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppb536-b539, January-2026, Available at : http://www.jetir.org/papers/JETIR2601178.pdf