Using Nuances of Emotion to Identify Personality
This work addresses personality identification for applications like psychology or social media analysis, but it is incremental as it builds on existing methods with new feature types.
The paper tackled personality detection from essays by introducing fine emotion categories as features, showing they provide statistically significant improvements over a baseline using lexical and coarse affect features.
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 correlations with 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 are significant indicators of personality. Additionally, we perform experiments to show that the gains provided by the fine affect categories are not obtained by using coarse affect categories alone or with specificity features alone. We employ these features in five SVM classifiers for detecting five personality traits through essays. We find that the use of fine emotion features leads to statistically significant improvement over a competitive baseline, whereas the use of coarse affect and specificity features does not.