Personality facets recognition from text

TL;DR

This paper explores recognizing specific personality facets from text using a low-cost model trained on data labeled with a shorter personality inventory, addressing limitations of existing models that focus only on broad traits.

Contribution

It introduces a novel low-cost approach for personality facets recognition from text using limited labeling data from shorter inventories.

Findings

Provides reference results for personality facets recognition

Demonstrates feasibility of low-cost facet recognition model

Addresses limitations of trait-only models

Abstract

Fundamental Big Five personality traits (e.g., Extraversion) and their facets (e.g., Activity) are known to correlate with a broad range of linguistic features and, accordingly, the recognition of personality traits from text is a well-known Natural Language Processing task. Labelling text data with facets information, however, may require the use of lengthy personality inventories, and perhaps for that reason existing computational models of this kind are usually limited to the recognition of the fundamental traits. Based on these observations, this paper investigates the issue of personality facets recognition from text labelled only with information available from a shorter personality inventory. In doing so, we provide a low-cost model for the recognition of certain personality facets, and present reference results for further studies in this field.