# Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier

arXiv: https://arxiv.org/abs/2205.13780 · HTML: https://mlsanity.com/paper/2205.13780/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 101 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 12 of 12)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Attention Models in Graphs: A Survey](https://mlsanity.com/paper/1807.07984/) (2018)
- [Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings](https://mlsanity.com/paper/1911.09304/) (2019)
- [Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles](https://mlsanity.com/paper/2010.01309/) (2020)
- [Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling](https://mlsanity.com/paper/2007.04571/) (2020)
- [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)

## Built on by (most cited first, 8 of 8)

- [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)
- [HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection](https://mlsanity.com/paper/2510.09893/) (2025)
- [Enhancing Personality Recognition in Dialogue by Data Augmentation and Heterogeneous Conversational Graph Networks](https://mlsanity.com/paper/2401.05871/) (2024)
- [Mind Reading or Misreading? LLMs on the Big Five Personality Test](https://mlsanity.com/paper/2511.23101/) (2025)
- [HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction](https://mlsanity.com/paper/2605.17355/) (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)

Nowadays, a tremendous amount of human communications occur on Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate their relationships. To this end, this paper aims to propose KGrAt-Net, which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2,467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP.