# Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

arXiv: https://arxiv.org/abs/2007.04571 · HTML: https://mlsanity.com/paper/2007.04571/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features](https://mlsanity.com/paper/2204.04629/) (2022)
- [Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier](https://mlsanity.com/paper/2205.13780/) (2022)
- [Continuous Output Personality Detection Models via Mixed Strategy Training](https://mlsanity.com/paper/2406.16223/) (2024)
- [Knowledge Graph-Enabled Text-Based Automatic Personality Prediction](https://mlsanity.com/paper/2203.09103/) (2022)
- [Text-based automatic personality prediction: A bibliographic review](https://mlsanity.com/paper/2110.01186/) (2021)
- [PsyAttention: Psychological Attention Model for Personality Detection](https://mlsanity.com/paper/2312.00293/) (2023)
- [Twenty Years of Personality Computing: Threats, Challenges and Future Directions](https://mlsanity.com/paper/2503.02082/) (2025)
- [RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation](https://mlsanity.com/paper/2401.10940/) (2024)
- [MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers](https://mlsanity.com/paper/2504.21427/) (2025)
- [Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing](https://mlsanity.com/paper/2105.11798/) (2021)
- [PersonaTAB: Predicting Personality Traits using Textual, Acoustic, and Behavioral Cues in Fully-Duplex Speech Dialogs](https://mlsanity.com/paper/2505.14356/) (2025)
- [HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction](https://mlsanity.com/paper/2605.17355/) (2026)

## Abstract (arXiv)

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.