Papers › Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling
Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling
Majid Ramezani, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar, Meysam Asgari-Chenaghlu, Ali-Reza Feizi-Derakhshi, Narjes Nikzad-Khasmakhi, Mehrdad Ranjbar-Khadivi, Zoleikha Jahanbakhsh-Nagadeh, Elnaz Zafarani-Moattar, Taymaz Rahkar-Farshi
Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Personality Trait Recognition | Essays | Ensemble Modeling | Accuracy | 60.24 | #1 of 2 | Archive leaderboard | report |
| Personality Trait Recognition | Essays | Ensemble Modeling | F-Measure | 61.04 | #1 of 2 | Archive leaderboard | report |
| Personality Trait Recognition | Essays | Ensemble Modeling | Precision | 60.48 | #1 of 2 | Archive leaderboard | report |
| Personality Trait Recognition | Essays | Ensemble Modeling | Recall | 61.66 | #1 of 2 | Archive leaderboard | report |
| Personality Trait Recognition | Essays | Ontology-Based | Accuracy | 51.42 | #2 of 2 | Archive leaderboard | report |
| Personality Trait Recognition | Essays | Ontology-Based | F-Measure | 67.91 | #2 of 2 | Archive leaderboard | report |
| Personality Trait Recognition | Essays | Ontology-Based | Precision | 51.42 | #2 of 2 | Archive leaderboard | report |
| Personality Trait Recognition | Essays | Ontology-Based | Recall | 100 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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