Using Nuances of Emotion to Identify Personality
Saif M. Mohammad, Svetlana Kiritchenko
Abstract
Past work on personality detection has shown that frequency of lexical categories such as first person pronouns, past tense verbs, and sentiment words have significant correlationswith personality traits. In this paper, for the first time, we show that fine affect(emotion) categories such as that of excitement, guilt, yearning, and admiration aresignificant indicators of personality. Additionally, we perform experiments to show thatthe gains provided by the fine affect categories are not obtained by using coarse affectcategories alone or with specificity features alone. We employ these features in five SVMclassifiers for detecting five personality traits through essays. We find that the use offine emotion features leads to statistically significant improvement over a competitivebaseline, whereas the use of coarse affect and specificity features does not.