# Using Nuances of Emotion to Identify Personality

arXiv: https://arxiv.org/abs/1309.6352 · HTML: https://mlsanity.com/paper/1309.6352/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2013.

## Built on by (most cited first, 6 of 6)

- [SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://mlsanity.com/paper/2101.10038/) (2021)
- [Fine-Grained Emotion Prediction by Modeling Emotion Definitions](https://mlsanity.com/paper/2107.12135/) (2021)
- [EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection](https://mlsanity.com/paper/2406.16079/) (2024)
- [Text-based automatic personality prediction: A bibliographic review](https://mlsanity.com/paper/2110.01186/) (2021)
- [Twenty Years of Personality Computing: Threats, Challenges and Future Directions](https://mlsanity.com/paper/2503.02082/) (2025)
- [Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation](https://mlsanity.com/paper/2407.10091/) (2024)

## Abstract (arXiv)

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.