  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%.
