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Sentiment and lexical analyses are widely used to detect depression oranxiety disorders. It has been documented that there are significantdifferences in the language used by a person with emotional disorders incomparison to a healthy individual. Still, the effectiveness of these lexicalapproaches could be improved further because the current analysis focuses onwhat the social media entries are about, and not how they are written. In thisstudy, we focus on aspects in which these short texts are similar to eachother, and how they were created. We present an innovative approach to thedepression screening problem by applying Collgram analysis, which is a knowneffective method of obtaining linguistic information from texts. We comparethese results with sentiment analysis based on the BERT architecture. Finally,we create a hybrid model achieving a diagnostic accuracy of 71%.

Hybrid approach to detecting symptoms of depression in social media entries