Hybrid approach to detecting symptoms of depression in social media entries
This work addresses the need for more effective depression screening tools for mental health applications, though it is incremental as it builds on existing lexical and sentiment analysis methods.
The study tackled the problem of detecting depression symptoms in social media entries by combining Collgram analysis with BERT-based sentiment analysis, resulting in a hybrid model that achieved a diagnostic accuracy of 71%.
Sentiment and lexical analyses are widely used to detect depression or anxiety disorders. It has been documented that there are significant differences in the language used by a person with emotional disorders in comparison to a healthy individual. Still, the effectiveness of these lexical approaches could be improved further because the current analysis focuses on what the social media entries are about, and not how they are written. In this study, we focus on aspects in which these short texts are similar to each other, and how they were created. We present an innovative approach to the depression screening problem by applying Collgram analysis, which is a known effective method of obtaining linguistic information from texts. We compare these results with sentiment analysis based on the BERT architecture. Finally, we create a hybrid model achieving a diagnostic accuracy of 71%.