Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hybrid approach to detecting symptoms of depression in social media
  entries   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%.

11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat