Computational personality recognition from Facebook text: psycholinguistic features, words and facets

Abstract

ABSTRACT Advances in the Natural Language Processing (NLP) and machine learning fields have led to the development of automated methods for the recognition of personality traits from text available from social media and similar sources. Systems of this kind exploit the close relation between lexical knowledge and personality models – such as the well-known Big Five model – to provide information about the author of an input text in a non-intrusive fashion, and at a low cost. Although now a well-established research topic in the field, the computational recognition of personality traits from text still leaves a number of research questions worth further exploration. In particular, this paper attempts to shed light on three main issues: (i) whether we may develop psycholinguistics-motivated models of personality recognition when such knowledge sources are not available for the target language under consideration; (ii) whether the use of psycholinguistic knowledge may be still superior to contemporary word vector representations; and (iii) whether we may infer certain personality facets from a corpus that does not explicitly convey this information. In this paper these issues are dealt with in a series of individual experiments of personality recognition from Facebook text, whose initial results should aid the future development of more robust systems of this kind.