Early Detection of Stress and Anxiety Using NLP and Machine Learning on Social Media Data

Stress and anxiety are some of the most public mental health illnesses that people in the current society face. It is important to determine these conditions early to be able to effectively promote the well-being of individuals. This research work presents the possibility of identifying stress and anxiety through social media (SM) data and an anonymous survey, by machine learning (ML) and natural language processing (NLP). The paper starts with data collection, using the DASS-21 questionnaire and a sample of tweets obtained from Twitter users from India, aimed at determining which language is associated with stress and anxiety. The gathered data is pre-processed in some of the steps, such as URL removal, lower casing, punctuation removal, stop words removal, and lemmatization. After data preprocessing, the textual content is transformed into numerical form through Word2Vec to facilitate pattern analysis. To enrich the analysis of the main topics in the dataset, the Latent Dirichlet Allocation (LDA) and the Non-Negative Matrix Factorization (NMF) techniques are applied. For the classification, the work uses ML algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) networks. Lastly, the project involves an application created with Streamlit to allow the user to interact with the model.