HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction
The article presents HyperPersona, a novel framework for text-based automatic personality prediction. It utilizes a multi-level hypergraph structure to model the hierarchical organization of text, enabling better inference of personality traits from linguistic behavior. Experimental results indicate that HyperPersona outperforms existing methods by effectively integrating multi-level linguistic cues.
- ▪HyperPersona models text using a multi-level hypergraph structure, capturing document, sentence, and word levels.
- ▪The framework allows for joint modeling of global, local, and lexical dependencies in text.
- ▪Experiments demonstrate that HyperPersona achieves superior performance in predicting the Big Five personality dimensions compared to state-of-the-art methods.
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Computer Science > Artificial Intelligence arXiv:2605.17355 (cs) [Submitted on 17 May 2026] Title:HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction Authors:Sina Heydari, Majid Ramezani View a PDF of the paper titled HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction, by Sina Heydari and Majid Ramezani View PDF HTML (experimental) Abstract:As a modern commodity, language has become a vast repository of socially and psychologically significant traits and concepts, reflecting the ways people encode pattern of thoughts, behaviors, and emotions into words.
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