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

TL;DR

This paper investigates how lexical-semantic resources, especially for sense disambiguation and semantic categorization, can enhance automatic personality classification without relying on personality-specific resources.

Contribution

It introduces three types of lexical-semantic features that capture high-level concepts and emotions, improving personality classification performance.

Findings

Comparable results to state-of-the-art methods

No need for personality-specific resources

Semantic features help overcome lexical gaps

Abstract

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.