Sentiment analysis for identifying depression through social media texts using machine learning technique

This paper presents a detailed exploration of the evolving landscape of depression detection through Sentiment Analysis (SA) in online communication platforms. With depression being a widespread and often undetected mental health concern, leveraging technology for early intervention is crucial. The study delves into three key approaches: lexicon-based methods, machine learning algorithms, and hybrid models, providing a thorough analysis of their strengths and limitations. It traces the historical evolution of SA, highlighting pivotal advancements, including deep learning techniques and multimodal data integration. The paper emphasizes the challenges, such as privacy concerns and algorithmic biases, and proposes future research directions, emphasizing multi-lingual analysis and interdisciplinary collaboration. The findings underscore the transformative potential of SA in reshaping mental health interventions and fostering inclusivity in support systems. Depression is a widespread challenge, often difficult to detect and monitor effectively. This paper explores how we can better understand and support individuals experiencing depression through SA. We delve into various methods used to analyze the emotions expressed in text, speech, and behaviour to identify signs of depression. We focus on the importance of spotting these signs early, assessing risks, and tailoring support for each person. Ethical considerations and the limitations of these analyses are [۱] carefully examined. Moreover, we discuss how we can advance these methods to improve mental health care. By looking closely at the current methods and their practical use, we aim to shed light on SA's role in caring for mental health. The goal is to emphasize the need for ongoing research and innovation to make these analyses even more effective in monitoring and supporting individuals dealing with depression.

کلیدواژه ها:

Depression detection ، Sentiment analysis ، Lexicon-Based Methods ، Machine Learning Algorithms ، Hybrid models