Too neurotic, not too friendly: Structured personality classification on textual data

12Citations
Citations of this article
37Readers
Mendeley users who have this article in their library.

Abstract

Personality plays a fundamental role in human interaction. With the increasing amount of online user-generated content, automatic detection of a person's personality based on the text she produces is an important step to labeling and analyzing human behavior at a large scale. To date, most approaches to personality classification have modeled each personality trait in isolation (e.g., independent binary classification). In this paper, we instead model the dependencies between different personality traits using conditional random fields. Our study finds a correlation between Agreeableness and Emotional Stability traits that can improve Agreeableness classification. However, we also find that accuracy on other traits can degrade with this approach, due in part to the overall problem difficulty. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

Cite

CITATION STYLE

APA

Iacobelli, F., & Culotta, A. (2013). Too neurotic, not too friendly: Structured personality classification on textual data. In AAAI Workshop - Technical Report (Vol. WS-13-01, pp. 19–22). AI Access Foundation. https://doi.org/10.1609/icwsm.v7i2.14472

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free