Personality plays a fundamental role in human interaction. With the increasing posting of text on the internet, 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 feature representations of the text to produce output classifications. In this paper we use structured classification approaches that learn and model both feature representations of text and dependencies between output labels (i.e. personality traits). Our study finds that there seems to be a correlation between Agreeableness and Emotional Stability and that it may be helping boost accuracy for Agreeableness when compared to more traditional approaches for supervised classification.


  author = 	 {Francisco Iacobelli and {\bf Aron Culotta} },
  title = 	 {Too Neurotic, Not too Friendly: Structured Personality Classification on Textual Data },
  booktitle = {ICWSM Workshop on Personality Classification},
  mytype = {Refereed Workshop Publications},
  year = 	 2013