Sentiment analysis for the detection of depressive users on social networks

Sentiment analysis for the detection of depressive users on social networks

Depression affects approximately 121 million people worldwide and has as significant an influence on patients as it does their carers. Until recently, psychologists used paper based psychological batteries for screening. New developments in Natural Language Processing however have enabled real-time analysis, offering increases in scalability and accessibility. We built on current knowledge by developing several Centralised Neural Network models around a pre-labelled Reddit dataset, first comparing performance. Our best model then parsed a Reddit forum (r/Depression) labelling depressed/control posts, evaluating them for changes over time whilst using a Term Frequency-Inverse Document Frequency (TF-IDF) tool to screen them for a weighted keyword list. We found neither word count nor the number of posts significantly affected our model’s performance, 67% of initial forum posts had depressed labels and as time progressed there was a decrease in depressed labels (per post) which was significant between users. Our TF-IDF tool also demonstrated a new way of looking at keywords, presenting us with a list most relevant to each category, whilst we additionally developed a free research tool for release into the public. Our study was able to yield support for its use within online forums at a very low cost; justifying further exploration into the use of AI tools for the screening of depression and other mood disorders.

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