Depression Analysis of Twitter Dataset Using Natural Language Processing Techniques

A significant global public health problem that affects many people is depression. Twitter has grown in popularity as a venue for people to express their feelings and experiences, making it a valuable source of information for figuring out the attitudes and trends around depression. We employed Multinomial Naive Bayes, Support Vector Machines, and Logistic Regression as well as three machine learning models to analyse this data. Our goal was to categorise tweets into positive and negative attitudes, find the most frequently used terms and phrases, and investigate how these patterns varied depending on the gender, age, and location of the tweets.