Paper Key : IRJ************859
Author: Bandi Shivani
Date Published: 18 Mar 2024
Abstract
Abstract:Depression has become a serious problem in this current generation and the number of people affected by depression is increasing day by day. However, some of them manage to acknowledge that they are facing depression while some of them do not know it. On the other hand, the vast progress of social media is becoming their diary to share their state of mind. Several kinds of research had been conducted to detect depression through the user post on social media using machine learning algorithms. Through the data available on social media, the researcher can able to know whether the users are facing depression or not. Machine learning algorithm enables to classify the data into correct groups and identify the depressive and non-depressive data. The proposed research work aims to detect the depression of the user by their data, which is shared on social media. The Twitter data is then fed into two different types of classifiers, which are Nave Bayes and a hybrid model, NBTree. The results will be compared based on the highest accuracy value to determine the best algorithm to detect depression. The results shows both algorithm perform equally by proving same accuracy level. Depression is a pervasive mental health issue affecting millions worldwide, with a significant impact on individualswell-being and society at large. Recognizing the gravity of this global concern, there is a growing interest in leveraging advanced technologies, such as Machine Learning (ML), to enhance our understanding and management of depression. This project aims to contribute to the field by employing ML techniques to analyze and predict depression-related factors, ultimately striving to offer valuable insights for early detection and personalized intervention strategies
DOI LINK : 10.56726/IRJMETS50111 https://www.doi.org/10.56726/IRJMETS50111