Identification of potential depression in social media posts

Abstract

The widespread use of social media to convey emotions (including depression) can be used to identify suspected depression in social media posts by examining the language that they have used on social media. This study aims to develop a system for detecting suspected depression in social media posts using sentiment analysis. This study collected data from X (Twitter) for three months using the keywords depression, mental health, and mental disorders. 1,502 data were generated due to the cleaning process of the 5,000 data collected. The findings of employing the validated by psychologist valence aware dictionary and sentiment reasoner (VADER) and Indonesian sentiment (InSet) lexicons demonstrate that VADER is more accurate (95.1%) than Inset (76.9%). The results of modeling with random forest, naive Bayes, and support vector machine (SVM) showed that random forest had the highest accuracy (83.3%), followed by naive Bayes (80.5%) and SVM (80.4%). Predicting social media data using lexicons and machine learning has limits that can be addressed by validation from clinical psychology. The frequency, timing, and idiom of posts on social media can reveal signs of depression. Depression seems to be best described by words like melancholy, stress, sadness, worthlessness, and depression.