Enhancing Mental Health Awareness through Twitter Analysis: A Comparative Study of Machine Learning and Hybrid Deep Learning Techniques

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Published: Oct 27, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9.8325
Keywords:
Convolution Neural Network(CNN), Depressed Keywords, Long Short Term Memory (LSTM), Random Forest (RF), Twitter, VADER

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Rohini Kancharapu
Gayatri Vidya Parishad College of Engineering for Women CSE Department, Kommadhi Visakhapatnam, India
Sri Nagesh Ayyagari
Rayapati Venkata Rangarao & Jagarlamudi Chandramouli College of Engineering, CSE Department, Chowdavaram Guntur, India

Abstract

This study explores the utilization of social media data, specifically tweets and comments, for gaining insights into individuals' mental health conditions. The objective is to enhance mental health awareness and enable early detection and intervention. Twitter data is collected using depression-related keywords, and two models are employed: a Random Forest model with TF-IDF and a hybrid CNN-LSTM model incorporating word2vec. The performance of the CNN-LSTM model surpasses that of the Random Forest model, achieving an accuracy rate of 89.4%. Furthermore, a user interface is developed to analyze users' Twitter profiles based on their tweets, allowing for potential intervention through automated reply messages. By harnessing social media data and advanced machine learning techniques, this research contributes to improving mental health awareness and timely addressing of mental health concerns.

Article Details

How to Cite
Kancharapu, R. ., & Ayyagari, S. N. . (2023). Enhancing Mental Health Awareness through Twitter Analysis: A Comparative Study of Machine Learning and Hybrid Deep Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 105–117. https://doi.org/10.17762/ijritcc.v11i9.8325
Issue
Vol. 11 No. 9 (2023)
Section
Articles

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