# Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles

arXiv: https://arxiv.org/abs/2010.01309 · HTML: https://mlsanity.com/paper/2010.01309/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2020.

## Built on by (most cited first, 20 of 20)

- [Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features](https://mlsanity.com/paper/2204.04629/) (2022)
- [The Imitation Game: Detecting Human and AI-Generated Texts in the Era of ChatGPT and BARD](https://mlsanity.com/paper/2307.12166/) (2023)
- [Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier](https://mlsanity.com/paper/2205.13780/) (2022)
- [Rediscovering the Latent Dimensions of Personality with Large Language Models as Trait Descriptors](https://mlsanity.com/paper/2409.09905/) (2024)
- [Explaining Translationese: why are Neural Classifiers Better and what do they Learn?](https://mlsanity.com/paper/2210.13391/) (2022)
- [Can LLMs Generate Behaviors for Embodied Virtual Agents Based on Personality Traits?](https://mlsanity.com/paper/2508.21087/) (2025)
- [Knowledge Graph-Enabled Text-Based Automatic Personality Prediction](https://mlsanity.com/paper/2203.09103/) (2022)
- [Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents](https://mlsanity.com/paper/2602.01063/) (2026)
- [Text-based automatic personality prediction: A bibliographic review](https://mlsanity.com/paper/2110.01186/) (2021)
- [Prompt-based System for Personality and Interpersonal Reactivity Prediction](https://mlsanity.com/paper/2203.12481/) (2022)
- [PsyAttention: Psychological Attention Model for Personality Detection](https://mlsanity.com/paper/2312.00293/) (2023)
- [Multitask Learning for Emotion and Personality Detection](https://mlsanity.com/paper/2101.02346/) (2021)
- [Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus](https://mlsanity.com/paper/2210.06405/) (2022)
- [Twenty Years of Personality Computing: Threats, Challenges and Future Directions](https://mlsanity.com/paper/2503.02082/) (2025)
- [A Quantitative and Qualitative Analysis of Schizophrenia Language](https://mlsanity.com/paper/2201.10430/) (2022)
- [An Attention-Based Denoising Framework for Personality Detection in Social Media Texts](https://mlsanity.com/paper/2311.09945/) (2023)
- [Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?](https://mlsanity.com/paper/2110.05035/) (2021)
- [Relationship Between Online Harmful Behaviors and Social Network Message Writing Style](https://mlsanity.com/paper/2212.07526/) (2022)
- [Personality Style Recognition via Machine Learning: Identifying Anaclitic and Introjective Personality Styles from Patients' Speech](https://mlsanity.com/paper/2311.04088/) (2023)
- [HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction](https://mlsanity.com/paper/2605.17355/) (2026)

## Abstract (arXiv)

Recently, the automatic prediction of personality traits has received increasing attention and has emerged as a hot topic within the field of affective computing. In this work, we present a novel deep learning-based approach for automated personality detection from text. We leverage state of the art advances in natural language understanding, namely the BERT language model to extract contextualized word embeddings from textual data for automated author personality detection. Our primary goal is to develop a computationally efficient, high-performance personality prediction model which can be easily used by a large number of people without access to huge computation resources. Our extensive experiments with this ideology in mind, led us to develop a novel model which feeds contextualized embeddings along with psycholinguistic features toa Bagged-SVM classifier for personality trait prediction. Our model outperforms the previous state of the art by 1.04% and, at the same time is significantly more computationally efficient to train. We report our results on the famous gold standard Essays dataset for personality detection.