Elamur Research⌕
☰
home / papers / 2026 / Big Five Personality Prediction through Emotion-Conditioned Representations and Learnable Psycholinguistic Mapping

Big Five Personality Prediction through Emotion-Conditioned Representations and Learnable Psycholinguistic Mapping

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

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/2ybdpcem2ohv

@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}
}

References

55 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Md Ali Akber, Tahira Ferdousi, Rasel Ahmed, Risha Asfara, Raqeebir Rab, and Umme Zakia. 2024. Personality and emotion—a comprehensive analysis using contextual text embeddings. Natural Language Processing Journal, 9:100105. find
  2. Süleyman Aslan and Uğur Güdükbay. 2019. Multimodal video-based apparent personality recognition using long short-term memory and convolutional neural networks. arXiv preprint arXiv:1911.00381. — [arXiv:1911.00381](https://arxiv.org/abs/1911.00381) find
  3. Erik Cambria, Yang Li, Frank Z Xing, Soujanya Poria, and Kenneth Kwok. 2020. Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis. In Proceedings of the 29th ACM international conference on information & knowledge management, pages 105–114. find
  4. Erik Cambria, Andrew Livingstone, and Amir Hussain. 2012. The hourglass of emotions. In Cognitive behavioural systems: COST 2102 international training school, dresden, Germany, February 21-26, 2011, revised selected papers, pages 144–157. Springer. find
  5. Charles M Ching, A Timothy Church, Marcia S Katigbak, Jose Alberto S Reyes, Junko Tanaka-Matsumi, Shino Takaoka, Hengsheng Zhang, Jiliang Shen, Rina Mazuera Arias, Brigida Carolina Rincon, et al. 2014. The manifestation of traits in everyday behavior and affect: A five-culture study. Journal of Rese… find
  6. Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, and Aaron C. Courville. 2017. Modulating early visual processing by language. In Advances in Neural Information Processing Systems (NeurIPS). Conditional Batch Normalization. find
  7. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019a. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT. find
  8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019b. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies… find
  9. John M Digman. 1990. Personality structure: Emergence of the five-factor model. Annual review of psychology, 41(1):417–440. find
  10. Adithya V Ganesan, Yash Kumar Lal, August Håkan Nilsson, and H Andrew Schwartz. 2023. Systematic evaluation of gpt-3 for zero-shot personality estimation. arXiv preprint arXiv:2306.01183. find
  11. Alan S Gerber, Gregory A Huber, David Doherty, Conor M Dowling, and Shang E Ha. 2010. Personality and political attitudes: Relationships across issue domains and political contexts. American Political Science Review, 104(1):111– 133. find
  12. Fatima Habib, Zeeshan Ali, Akbar Azam, Komal Kamran, and Fahad Mansoor Pasha. 2024. Navigating pathways to automated personality prediction: a comparative study of small and medium language models. Frontiers in Big Data, 7:1387325. find
  13. Yimin He, M Brent Donnellan, and Anjelica M Mendoza. 2019. Five-factor personality domains and job performance: A second order meta-analysis. Journal of Research in Personality, 82:103848. find
  14. Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for nlp. In International conference on machine learning, pages 2790–2799. PMLR. find
  15. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations (ICLR). find
  16. Dipika Jain, Akshi Kumar, and Rohit Beniwal. 2022. Personality bert: A transformer-based model for personality detection from textual data. In Proceedings of international conference on computing and communication networks: ICCCN 2021, pages 515–522. Springer. find
  17. Oliver P John, Richard W Robins, and Lawrence A Pervin. 2010. Handbook of personality: Theory and research. Guilford Press. find
  18. Oliver P John, Sanjay Srivastava, et al. 1999. The big-five trait taxonomy: History, measurement, and theoretical perspectives. find
  19. Weixi Kang, Francois Steffens, Sònia Pineda, Kaya Widuch, and Antonio Malvaso. 2023. Personality traits and dimensions of mental health. Scientific Reports, 13(1):7091. find
  20. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942. find
  21. Yang Li, Amirmohammad Kazemeini, Yash Mehta, and Erik Cambria. 2022. Multitask learning for emotion and personality traits detection. Neurocomputing, 493:340–350. find
  22. Zheng Li, Sujian Li, Dawei Zhu, Qilong Ma, and Weimin Xiong. 2025. EERPD: Leveraging emotion and emotion regulation for improving personality detection. In Proceedings of the 31st International Conference on Computational Linguistics, pages 7721–7734, Abu Dhabi, UAE. Association for Computational Li… find
  23. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. find
  24. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In International Conference on Learning Representations (ICLR). find
  25. Navonil Majumder, Soujanya Poria, Alexander Gelbukh, and Erik Cambria. 2017. Deep learning-based document modeling for personality detection from text. IEEE intelligent systems, 32(2):74– 79. find
  26. GJ Molloy, RE O’carroll, and Eamonn Ferguson. 2014. Conscientiousness and medication adherence: A meta-analysis. Annals of Behavioral Medicine, 47(1):92–101. find
  27. Anam Naz, Hikmat Ullah Khan, Amal Bukhari, Bader Alshemaimri, Ali Daud, and Muhammad Ramzan. 2025. Machine and deep learning for personality traits detection: a comprehensive survey and open research challenges. Artificial Intelligence Review, 58(8):239. find
  28. Vincent Oh and Eddie MW Tong. 2020. Negative emotion differentiation and long-term physical health—the moderating role of neuroticism. Health Psychology, 39(2):127. find
  29. Gregory Park, H Andrew Schwartz, Johannes C Eichstaedt, Margaret L Kern, Michal Kosinski, David J Stillwell, Lyle H Ungar, and Martin EP Seligman. 2015. Automatic personality assessment through social media language. Journal of personality and social psychology, 108(6):934. find
  30. James W Pennebaker, Martha E Francis, Roger J Booth, et al. 2001. Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates, 71(2001):2001. find
  31. Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron Courville. 2018. Film: Visual reasoning with a general conditioning layer. In Proceedings of the AAAI Conference on Artificial Intelligence. ArXiv:1709.07871. find our card
  32. Heinrich Peters and Sandra C Matz. 2024. Large language models can infer psychological dispositions of social media users. PNAS nexus, 3(6):pgae231. find
  33. Marco Piastra and Patrizia Catellani. 2025. On the emergent capabilities of chatgpt 4 to estimate personality traits. Frontiers in Artificial Intelligence, 8:1484260. find
  34. Robert Plutchik. 2001. The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. American scientist, 89(4). find
  35. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for C… find
  36. Zhancheng Ren, Qiang Shen, Xiaolei Diao, and Hao Xu. 2021. A sentiment-aware deep learning approach for personality detection from text. Information Processing & Management, 58(3):102532. find
  37. William Revelle and Klaus R Scherer. 2009. Personality and emotion. Oxford companion to emotion and the affective sciences, 1:304–306. find
  38. Justin D Russell, Carl F Weems, Irfan Ahmed, and Golden G Richard III. 2017. Self-reported secure and insecure cyber behaviour: factor structure and associations with personality factors. Journal of Cyber Security Technology, 1(3-4):163–174. find
  39. Kit-May Shum, Michal Ptaszynski, and Fumito Masui. 2025. Big five personality trait prediction based on user comments. Information, 16(5):418. find
  40. Aleksandra Sorokovikova, Natalia Fedorova, Sharwin Rezagholi, and Ivan P Yamshchikov. 2024. Llms simulate big five personality traits: Further evidence. arXiv preprint arXiv:2402.01765. — [arXiv:2402.01765](https://arxiv.org/abs/2402.01765) find
  41. Luke Stark. 2023. Artificial intelligence and the conjectural sciences. BJHS Themes, 8. find
  42. Luke Stark and Jesse Hoey. 2021. The ethics of emotion in artificial intelligence systems. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery. find
  43. Luke Stark and Jevan Hutson. 2021. Physiognomic artificial intelligence. Fordham Intell. Prop. Media & Ent. LJ, 32. find
  44. Yosephine Susanto, Andrew G Livingstone, Bee Chin Ng, and Erik Cambria. 2020. The hourglass model revisited. IEEE Intelligent Systems, 35(5):96–102. find
  45. Maria Taramigkou, Dimitris Apostolou, and Gregoris Mentzas. 2018. Leveraging exploratory search with personality traits and interactional context. Information Processing & Management, 54(4):609–629. find
  46. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems. find
  47. Mehta Yash, Samin Fatehi, Amirmohammad Kazameini, Clemens Stachl, Erik Cambria, and Sauleh Eetemadi. 2020. Bottom-Up and Top-Down: Predicting Personality with Psycholinguistic and Language Model Features. In 2020 IEEE International Conference on Data Mining (ICDM), pages 1184–1189, Sorrento, Italy.… find
  48. Tuncay Yılmaz, Abdullah Ergil, and Bahar İlgen. 2019. Deep learning-based document modeling for personality detection from turkish texts. In Proceedings of the future technologies conference, pages 729–736. Springer. find
  49. Jianfeng Zhu, Julina Maharjan, Xinyu Li, Karin G. Coifman, and Ruoming Jin. 2025. Evaluating llm alignment on personality inference from real-world interview data. 10. find
  50. Language Resource References Erik Cambria and Yang Li and Frank Z. Xing and Soujanya Poria and Kenneth Kwok. 2020. SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis. find
  51. Erik Cambria, Soujanya Poria, Devamanyu Hazarika, and Kenneth Kwok. 2018. Senticnet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications… find
  52. Gjurković, Matej and Karan, Vanja Mladen and Vukojević, Iva and Bošnjak, Mihaela and Snajder, Jan. 2021. PANDORA Talks: Personality and Demographics on Reddit. Association for Computational Linguistics. find
  53. Jiang, Hang and Zhang, Xianzhe and Choi, Jinho D. 2020. Automatic text-based personality recognition on monologues and multiparty dialogues using attentive networks and contextual embeddings (student abstract). find
  54. James W Pennebaker and Laura A King. 1999. Linguistic styles: language use as an individual difference. Journal of personality and social psychology, 77(6):1296. find
  55. Tiuleneva, Marina and Porvatov, Vadim A. and Strapparava, Carlo. 2024. Big-Five Backstage: A Dramatic Dataset for Characters Personality Traits & Gender Analysis. ELRA and ICCL. find

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/zangari-2026-big-five-personality-prediction-through/