2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 15 , Issue 1 , PP: 196-204, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Madhu Sudhan H. V. 1 * , S. Saravana Kumar 2
Doi: https://doi.org/10.54216/FPA.150115
Depression, or Major Depressive Disorder, is a serious and common medical condition that affects people worldwide. It negatively affects the person's feelings, thoughts, and actions. Depression causes a loss of interest in activities he enjoyed in the past. It can lead to physical and emotional problems that hamper the daily activities at work and home. In recent years, much research has been done to identify Depression through various modalities of image, speech, and text through artificial intelligence. Social media is an important medium where many discussions and mentions happen about Depression. The current study proposes a novel approach to understand how the depressed and non-depressed communicate differently with the help of Topic Modeling with latent-Dirichlet allocation (LDA) and also detect depression with the help of Robustly Optimized BERT Pretraining Approach (RoBERTa). The current study achieved an accuracy of 66.4% for the depression detection model, which outperformed the previous approaches with similar methodology. The current study is helpful for self-diagnosis of signs of Depression at very early stages.
Clinical Depression , Artificial Intelligence , Machine Learning , Natural Language Processing , Mathematical Fusion , Bidirectional Encoder Representations from Transformers , Social media , Twitter , Reddit , Latent Dirichlet allocation , Fusion Based , Topic Modeling
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