Analyzing Online Conversations on Reddit: A Study of Stress and Anxiety Through Topic Modeling and Sentiment Analysis.

Abstract

This study analyses the topic of stress and anxiety in 3,765 Reddit posts to determine key themes and emotional undertones using natural language processing (NLP) techniques. Five major category topics are identified from the posts using the latent Dirichlet allocation (LDA) algorithm. The topics identified are general discontent and lack of direction; panic and anxiety attacks; physical symptoms of anxiety, stress, and mental health concerns; and seeking help for anxiety. Sentiment analysis with the help of TextBlob showed a neutral score, for the most part: an average polarity score of 0.009 and a subjectivity score of 0.494. Several kinds of visualizations, including word clouds, bar charts, and pie charts, have been used to show the distribution and importance of these topics. These findings underscore the important role played by online communities in extending their support to those in distress because of mental health problems. This information is very important to mental health professionals and researchers. This study shows the effectiveness of using a combination of topic modeling and sentiment analysis to identify problems related to mental health discussed on social media. These results direct the possibilities for future research in using advanced NLP techniques and expanding to larger datasets.