HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction

TL;DR

HyperPersona introduces a hierarchical hypergraph framework with transformer-based encoding to improve text-based automatic personality prediction by capturing multi-level linguistic structures.

Contribution

It is the first to explicitly model hierarchical text structure with hypergraphs for personality prediction, enhancing the integration of global, local, and lexical cues.

Findings

Outperforms state-of-the-art baselines on Big Five personality prediction.

Effectively captures multi-level linguistic cues from text.

Demonstrates the importance of hierarchical modeling in NLP tasks.

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. Text-based Automatic Personality Prediction (APP), seeks to infer personality from linguistic behavior, offering a scalable alternative to traditional psychometric assessments. Although text is inherently hierarchical, with the document-level capturing global features, the sentence-level encoding local semantics, and the word-level providing fine-grained lexical information, most existing approaches rely on shallow, sequential, or single-level representations that ignore the multi-level structure of written language. To address this, we propose HyperPersona, a framework that explicitly models the hierarchical organization of text (document, sentence, and word) through hypergraph structure, where a document and its sentences are represented as hyperedges, and the words are represented as nodes, enabling joint modeling of global, local, and lexical dependencies of text. Followed by a transformer-based graph encoder that learns interactions within and across these linguistic layers, yielding context-sensitive and structurally grounded feature representations for personality prediction. Experiments on the Big Five personality dimensions show that, while relying solely on text, HyperPersona effectively integrates multi-level linguistic cues, achieving superior performance compared to state-of-the-art baselines. These findings underscore the critical role of textual hierarchy in advancing human-like personality inference from natural language.