Machine Learning Techniques for Deleting Human Depression Using Social Media Data

Abstract

Depression is one of the most serious issue among human civilization in modern time. It is one of the well-known mental health issues in this era. In this paper we worked on this metal health issue known as depression. This has affected countless people of different gender, age and race. It is also taboo for some people to talk about which makes it more so serious. Now people also share their depression thoughts through the social media. It is also to be mentioned many people does not even realize they are depressed but their posts on social media shows they are. One of the social media where people share their thoughts are twitter and this is what we chose to gather our data from twitter. This method required both depressed and not depressed social media data so our algorithm can distinguish between depressed and not depressed post. NLP and Machine Learning is used for the process. This research aims to explore the use of machine learning techniques for detecting human depression using social media data. The study will focus on the use of various algorithms including TF-IDF, BOW, XGB Classifier, Random Forest Classifier, Logistic Regression, SVC, ADA Boost Classifier and Naive Bayes. The objective of this research is to develop a model that can accurately identify individuals who may be at risk for developing depression based on their social media data. The research will utilize a dataset of social media posts and interactions, which will be preprocessed and used to train and test the machine learning algorithms. The performance of each algorithm will be evaluated using metrics such as accuracy, precision, recall, and f1-score. The final model will be chosen based on the highest performance. The research will also consider the ethical aspects of using social media data for detecting depression, such as privacy concerns, accuracy and reliability, bias, access, and responsibility. The results of this research could have significant implications for the identification and treatment of depression, as well as for the overall well-being and quality of life of those affected. The results of this research will provide valuable insights into the use of machine learning techniques for detecting human depression using social media data. The findings will be useful for mental health professionals, researchers, and policymakers to understand the potential of social media data for the early identification of individuals at risk for depression. The research will also provide valuable information for the development of more accurate and efficient models for detecting depression in the future, which could ultimately lead to better outcomes for those affected and a reduction in the overall burden of depression on society.