  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.
