---
title: "Big Five Personality Prediction through — Zangari 2026"
description: "Predicting Big Five (OCEAN) traits from text usually relies only on linguistic signals, although psychology ties personality closely to how people…"
authors: ["Lorenzo Zangari", "Antonin Schnyder", "Davide Picca"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.647/"
doi: "10.63317/2ybdpcem2ohv"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zangari-2026-big-five-personality-prediction-through/"
---
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# Big Five Personality Prediction through Emotion-Conditioned Representations and Learnable Psycholinguistic Mapping

**Authors:** Lorenzo Zangari, Antonin Schnyder, Davide Picca · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.647/) · [PDF](https://aclanthology.org/2026.lrec-1.647.pdf) · [DOI 10.63317/2ybdpcem2ohv](https://doi.org/10.63317/2ybdpcem2ohv) · [OpenAlex](https://openalex.org/W7160130196)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Predicting Big Five (OCEAN) traits from text usually relies only on linguistic signals, although psychology ties personality closely to how people experience and express emotion. Zangari, Schnyder and Picca (University of Lausanne) build an emotion-aware framework on top of Pre-trained Language Models and treat OCEAN prediction as multi-label binary classification. Each text receives a four-dimensional affective vector from SenticNet, based on the revisited Hourglass of Emotions: Introspection, Temper, Aptitude and Sensitivity.

This vector feeds the model in two ways. First, a FiLM layer uses it to rescale and shift the PLM text embedding. Second, a learnable prior matrix links emotions to traits. The matrix starts from correlation-based psycholinguistic knowledge (after Yash et al., 2020) and is then refined during training. The framework supports parameter-efficient fine-tuning.

On several Big Five-annotated datasets from different domains, the method beat sentence-embedding baselines and Large Language Models. Average F1 gains were at least 15% in out-of-domain settings. The work shows that theory-grounded affective knowledge can be combined with data-driven learning for more robust personality inference.

## Key points
- **Affective features:** The model uses SenticNet 6 (Cambria et al., 2020) with four dimensions. This is a newer release than the SenticNet 5 used by Yash et al. (2020), and the framework can accept other SenticNet versions.
- **Two emotion-integration mechanisms:**
  - FiLM (Perez et al., 2018) conditioning, z = γ(e) ⊙ h + β(e), applied to PLM embeddings.
  - A learnable 4×5 emotion-to-trait prior, initialised from point-biserial correlations between SenticNet dimensions and binary trait labels.
- **Task setup:** Binary multi-label prediction over O, C, E, A and N. Trait-specific heads can be replaced with regression heads when trait scores are continuous.
- **Main result:** The framework outperformed sentence-embedding methods and LLMs, with an average F1 improvement of at least 15% out-of-domain.
- **Resources:** The paper's language-resource references list the Essays corpus (Pennebaker & King, 1999), PANDORA (Reddit), the Jiang et al. (2020) monologue/dialogue corpus and Big-Five Backstage (dramatic characters). The evaluation section was missing from the extracted text, so the exact dataset split could not be confirmed. Source code is released on GitHub.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Psycholinguistics & language acquisition](https://md.research.elamur.ai/free/en/topics/psycholinguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zangari2026big,
  title = {{Big Five Personality Prediction through Emotion-Conditioned Representations and Learnable Psycholinguistic Mapping}},
  author = {Zangari, Lorenzo and Schnyder, Antonin and Picca, Davide},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/2ybdpcem2ohv},
  url = {https://doi.org/10.63317/2ybdpcem2ohv}
}
```

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