Depression Intensity Estimation via social media: A Deep Learning Approach

Project Code :TCMAPY1070

Objective

The project aims to develop a deep learning model for estimating depression intensity through social media data analysis. By leveraging natural language processing and sentiment analysis techniques, the model seeks to provide accurate and timely assessments, enabling early intervention and support for individuals at risk of severe depressive symptoms.

Abstract

This study proposes a novel approach to estimate depression intensity using deep learning techniques on social media data. Leveraging a diverse dataset from multiple platforms, the model integrates natural language processing and sentiment analysis to gauge the severity of depression symptoms. By extracting linguistic patterns and emotional cues, the deep learning framework demonstrates robust performance in accurately assessing depression levels. The findings suggest the potential of automated analysis in monitoring mental health through online interactions, offering a promising avenue for early detection and intervention strategies, thus contributing to improved mental health care and support.

Keywords: Catboost, SVC, ANN, DNN and LSTM.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.