# Lexical-semantic resources: yet powerful resources for automatic personality classification

arXiv: https://arxiv.org/abs/1711.09824 · HTML: https://mlsanity.com/paper/1711.09824/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [On Text-based Personality Computing: Challenges and Future Directions](https://mlsanity.com/paper/2212.06711/) (2022)
- [Extroversion or Introversion? Controlling The Personality of Your Large Language Models](https://mlsanity.com/paper/2406.04583/) (2024)
- [Personality Editing for Language Models through Adjusting Self-Referential Queries](https://mlsanity.com/paper/2502.11789/) (2025)

## Abstract (arXiv)

In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams. Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.