Leveraging Algorithms for depression detection in natural language processing

Abstract

This research presents a comprehensive exploration of depression identification methodologies, employing a diverse array of classification algorithms, including Logistic Regression, Random Forest, Gradient Boosting, Multilayer Perceptron (MLP), and Long Short-Term Memory Networks (LSTMs). Utilizing a dataset comprising textual expressions, traditional machine learning models are juxtaposed against a deep learning paradigm, aiming to discern intricate patterns indicative of depression. Noteworthy outcomes emerge, with Logistic Regression and Random Forest achieving commendable accuracies of 95.60% and 95.69%, respectively. The study introduces an LSTM model, showcasing its potential in text-based depression identification, yielding an accuracy of 73.79%. Beyond quantitative assessments, the research delves into the societal impact, ethical considerations, and sustainability of the proposed models. Recognizing the significance of mental health awareness, this study contributes valuable insights into algorithmic frameworks for depression detection, fostering a nuanced understanding of their applicability, ethical considerations, and societal implications. The findings not only provide a comprehensive comparison of state-of-the-art models but also underscore the need for responsible deployment and sustainable practices in leveraging machine learning for mental health applications. As I navigate the complexities of mental health analysis, this research seeks to offer a holistic perspective, emphasizing ethical considerations and societal implications while opening avenues for future research and advancements in the domain.