# A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts

arXiv: https://arxiv.org/abs/1610.04345 · HTML: https://mlsanity.com/paper/1610.04345/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2016.

## Built on by (most cited first, 9 of 9)

- [Text-based automatic personality prediction: A bibliographic review](https://mlsanity.com/paper/2110.01186/) (2021)
- [On Text-based Personality Computing: Challenges and Future Directions](https://mlsanity.com/paper/2212.06711/) (2022)
- [Profile-LLM: Dynamic Profile Optimization for Realistic Personality Expression in LLMs](https://mlsanity.com/paper/2511.19852/) (2025)
- [Multitask Learning for Emotion and Personality Detection](https://mlsanity.com/paper/2101.02346/) (2021)
- [Extroversion or Introversion? Controlling The Personality of Your Large Language Models](https://mlsanity.com/paper/2406.04583/) (2024)
- [Myers-Briggs personality classification from social media text using pre-trained language models](https://mlsanity.com/paper/2207.04476/) (2022)
- [Personality Editing for Language Models through Adjusting Self-Referential Queries](https://mlsanity.com/paper/2502.11789/) (2025)
- [Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?](https://mlsanity.com/paper/2110.05035/) (2021)
- [HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction](https://mlsanity.com/paper/2605.17355/) (2026)

## Abstract (arXiv)

Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.