Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Language-independent and Compositional Model for Personality Trait
  Recognition from Short Texts   Many methods have been used to recognize author personality traits from text,
typically combining linguistic feature engineering with shallow learning
models, e.g. linear regression or Support Vector Machines. This work uses
deep-learning-based models and atomic features of text, the characters, to
build hierarchical, vectorial word and sentence representations for trait
inference. This method, applied to a corpus of tweets, shows state-of-the-art
performance across five traits and three languages (English, Spanish and
Italian) compared with prior work in author profiling. The results, supported
by preliminary visualisation work, are encouraging for the ability to detect
complex human traits.

17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph