# Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features

arXiv: https://arxiv.org/abs/2204.04629 · HTML: https://mlsanity.com/paper/2204.04629/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 10 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [PANDORA Talks: Personality and Demographics on Reddit](https://mlsanity.com/paper/2004.04460/) (2020)
- [NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets](https://mlsanity.com/paper/1308.6242/) (2013)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles](https://mlsanity.com/paper/2010.01309/) (2020)
- [Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models](https://mlsanity.com/paper/2106.08689/) (2021)
- [Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling](https://mlsanity.com/paper/2007.04571/) (2020)
- [DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques](https://mlsanity.com/paper/1810.03660/) (2018)
- [Prediction of Listener Perception of Argumentative Speech in a Crowdsourced Dataset Using (Psycho-)Linguistic and Fluency Features](https://mlsanity.com/paper/2111.07130/) (2021)

## Built on by (most cited first, 10 of 10)

- [Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation](https://mlsanity.com/paper/2306.01183/) (2023)
- [MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders](https://mlsanity.com/paper/2212.09855/) (2022)
- [(Psycho-)Linguistic Features Meet Transformer Models for Improved Explainable and Controllable Text Simplification](https://mlsanity.com/paper/2212.09848/) (2022)
- [On Text-based Personality Computing: Challenges and Future Directions](https://mlsanity.com/paper/2212.06711/) (2022)
- [PsyAttention: Psychological Attention Model for Personality Detection](https://mlsanity.com/paper/2312.00293/) (2023)
- [Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features](https://mlsanity.com/paper/2212.09465/) (2022)
- [Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media](https://mlsanity.com/paper/2212.09839/) (2022)
- [HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction](https://mlsanity.com/paper/2605.17355/) (2026)
- [Mechanistic Personality Analysis of LLMs: Steering Personality via Latent Feature Interventions](https://mlsanity.com/paper/2606.28770/) (2026)
- [Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition](https://mlsanity.com/paper/2607.08374/) (2026)

## Abstract (arXiv)

Research at the intersection of personality psychology, computer science, and linguistics has recently focused increasingly on modeling and predicting personality from language use. We report two major improvements in predicting personality traits from text data: (1) to our knowledge, the most comprehensive set of theory-based psycholinguistic features and (2) hybrid models that integrate a pre-trained Transformer Language Model BERT and Bidirectional Long Short-Term Memory (BLSTM) networks trained on within-text distributions ('text contours') of psycholinguistic features. We experiment with BLSTM models (with and without Attention) and with two techniques for applying pre-trained language representations from the transformer model - 'feature-based' and 'fine-tuning'. We evaluate the performance of the models we built on two benchmark datasets that target the two dominant theoretical models of personality: the Big Five Essay dataset and the MBTI Kaggle dataset. Our results are encouraging as our models outperform existing work on the same datasets. More specifically, our models achieve improvement in classification accuracy by 2.9% on the Essay dataset and 8.28% on the Kaggle MBTI dataset. In addition, we perform ablation experiments to quantify the impact of different categories of psycholinguistic features in the respective personality prediction models.