Depression Detection Using AI with Chatbot Support

Year : 2026 | Volume : 14 | Issue : 01 | Page : 01 08
    By

    S. A. Patil,

  • Anushka Sanjay Gaikwad,

  • Komal Ashok Gaikwad,

  • Sampada Nitin Aher,

  • Sakshi Babasaheb Mogal,

  1. Lecturer, Department of Computer Technology, Sanjivani K.B.P. Polytechnic, Kopargaon, Maharashtra, India
  2. Research Scholar, Department of Computer Technology, Sanjivani K.B.P. Polytechnic, Kopargaon, Maharashtra, India
  3. Research Scholar, Department of Computer Technology, Sanjivani K.B.P. Polytechnic, Kopargaon, Maharashtra, India
  4. Research Scholar, Department of Computer Technology, Sanjivani K.B.P. Polytechnic, Kopargaon, Maharashtra, India
  5. Research Scholar, Department of Computer Technology, Sanjivani K.B.P. Polytechnic, Kopargaon, Maharashtra, India

Abstract

Depression is a major global health concern and a significant contributor to suicide rates worldwide. India reports a high number of suicide cases, making the early detection of mental distress and depression essential for timely intervention. This research presents an AI-based system for depression detection that integrates deep learning, natural language processing (NLP), and a chatbot for user support. The system analyzes facial expressions using convolutional neural networks (CNNs) and assesses emotional states from textual input through machine-learning techniques such as Naïve Bayes and support vector machines (SVMs). The system captures real-time facial images through a camera, which are then processed using CNN algorithms and classified into emotional categories such as happy or sad. Simultaneously, it analyzes users’ language patterns to evaluate sentiment, stress levels, and depressive tendencies. To address class imbalance, the study employs the synthetic minority over-sampling technique (SMOTE), thereby improving the accuracy of depression detection. In addition, the system incorporates a chatbot interface that interacts with users, providing support and personalized recommendations based on the detected emotional state. The model was evaluated using datasets such as DAIC-WOZ and PHQ-8 and demonstrated strong performance in identifying depressive symptoms. The integration of facial-expression analysis and text-based sentiment assessment provides a comprehensive approach to depression detection, enhancing the accuracy and reliability of the diagnostic process.

Keywords: Mental Stress Detection, Speech Processing, CNN, NLP, Chatbot, Deep Learning, Machine Learning, Depression Prediction

[This article belongs to Research & Reviews: A Journal of Embedded System & Applications ]

How to cite this article:
S. A. Patil, Anushka Sanjay Gaikwad, Komal Ashok Gaikwad, Sampada Nitin Aher, Sakshi Babasaheb Mogal. Depression Detection Using AI with Chatbot Support. Research & Reviews: A Journal of Embedded System & Applications. 2025; 14(01):01-08.
How to cite this URL:
S. A. Patil, Anushka Sanjay Gaikwad, Komal Ashok Gaikwad, Sampada Nitin Aher, Sakshi Babasaheb Mogal. Depression Detection Using AI with Chatbot Support. Research & Reviews: A Journal of Embedded System & Applications. 2025; 14(01):01-08. Available from: https://journals.stmjournals.com/rrjoesa/article=2025/view=242057


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Regular Issue Subscription Review Article
Volume 14
Issue 01
Received 01/04/2025
Accepted 07/10/2025
Published 20/12/2025
Publication Time 263 Days
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