Papers with personality prediction
Incorporating Textual Information on User Behavior for Personality Prediction (P19-2)
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| Challenge: | Recent studies have shown that textual information of user posts and user behaviors are useful for predicting the personality of social media users. |
| Approach: | They propose to use textual information of user behaviors to predict personality of Twitter users by taking user behaviors into account. |
| Outcome: | The proposed models can predict personality of users who do not post frequently, while taking user behaviors into account. |
Learning to Answer Psychological Questionnaire for Personality Detection (2021.findings-emnlp)
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| Challenge: | Existing text-based personality detection research relies on data-driven approaches to implicitly capture personality cues in online posts lacking the guidance of psychological knowledge. |
| Approach: | They propose a model to capture key information in texts and a questionnaire to help the user to make a personality assessment. |
| Outcome: | The proposed model captures key information in texts and a questionnaire and can be used to improve personality prediction. |
MBTI Personality Prediction for Fictional Characters Using Movie Scripts (2022.findings-emnlp)
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| Challenge: | Existing NLP models cannot predict character's personality types based on text classifications . character comprehension is the cornerstone of understanding stories in psychology and education. |
| Approach: | They propose a benchmark to predict movie character's MBTI or Big 5 personality types based on the narratives of the character. |
| Outcome: | The proposed model outperforms existing models in the task and is more accurate than random guesses. |
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection (2025.coling-main)
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| Challenge: | Existing methods for personality detection ignore the connection between psychological knowledge “emotion regulation” and personality traits. |
| Approach: | They propose to use emotion regulation and emotion features to retrieve few-shot samples and provide process CoTs for inferring labels from text. |
| Outcome: | The proposed method outperforms SOTA by 15.05/4.29 on the two benchmark datasets. |