Utilizing Twitter Data and NLP to Analyze and Predict Public Sentiment Trends in Mental Health

The increase in the use and accessibility of social media platforms over the past few years has changed the way people share their thoughts, feelings, and experiences. Twitter, unlike Facebook, is unique for its real-time nature, meaning its users generate an enormous stream of data daily. This makes Twitter a unique platform for mental health researchers to investigate, not only as an additional source of data for understanding public sentiment towards mental health, but to examine mental health trends more closely and potentially even predict instances of a mental health crisis on a population level. This study examines the application of Twitter data, in conjunction with machine learning and natural language processing (NLP) methodologies, to examine public opinion and discourse pertaining to mental health. The objective is to reveal patterns and trends in the discussion of mental health on social media, providing valuable insights into the public’s view and awareness of mental health matters. This paper aims to explore popular perception regarding mental health issues by analyzing Twitter data. Our objective is to offer practical and valuable information that can guide the development of public health policies and interventions, ultimately improving the responsiveness and effectiveness of mental health treatment systems.