AI-Powered Mental State Analysis: Detecting Depression and Happiness
Nagashree K T1
Dept of Information Science and Engineering (ISE) AMCEC nagashreekt86@gmail.com
Punith SR 1, Siddesh G 2, Mohan Kumar MD 3, Vinith Kumar TC 4
Dept of information Science and Engineering (ISE)AMCEC
1am22is086@amceducation.in , 1am22is109@amceducation.in , 1am22is066@amceducation.in , 1am22is125@amceducation.in.
Abstract
Mental health issues such as depression are increasing worldwide and have become a major concern affecting individuals’ well-being and quality of life. Despite growing awareness, early detection of mental health conditions remains difficult due to social stigma, lack of awareness, and limited access to mental health professionals. At the same time, understanding positive emotional states like happiness is equally important for assessing overall mental well-being.
With rapid advancements in artificial intelligence, it is now possible to analyze human emotions through digital data. AI techniques can interpret emotional cues from text, speech, and facial expressions to identify mental states. This project focuses on developing an AI-powered system that can automatically analyze such data to detect depression and happiness in an efficient and non-intrusive manner.
The proposed system uses transformer-based natural language processing models to analyze textual patterns, CNN-LSTM models to capture emotional features from speech, and deep learning-based facial expression recognition to interpret visual cues. Each of these modalities generates an emotional assessment, which is then combined using a multimodal fusion approach to improve overall prediction accuracy and reliability.
The system is designed with a scalable and user-friendly architecture that supports real-time processing and easy interaction. It provides users with meaningful insights into their emotional state and helps in identifying early signs of mental health issues. By offering continuous monitoring rather than one-time assessment, the system enables better understanding of emotional trends over time.
Key words: Emotional Recognition, Depression Detection, Happiness Analysis, Neural Networks, Multimodal Analysis, Deep Learning.
