A Two-Model Approach for Humour Style Recognition

TL;DR

This paper introduces a new dataset and a two-model approach for recognising humour styles in text, significantly improving classification accuracy, especially between affiliative and aggressive styles.

Contribution

It presents the first dataset for humour style recognition and proposes a novel two-model method that enhances classification performance over existing approaches.

Findings

11.61% improvement in f1-score for affiliative humour

Consistent performance gains across 14 models

New tools for computational humour analysis

Abstract

Humour, a fundamental aspect of human communication, manifests itself in various styles that significantly impact social interactions and mental health. Recognising different humour styles poses challenges due to the lack of established datasets and machine learning (ML) models. To address this gap, we present a new text dataset for humour style recognition, comprising 1463 instances across four styles (self-enhancing, self-deprecating, affiliative, and aggressive) and non-humorous text, with lengths ranging from 4 to 229 words. Our research employs various computational methods, including classic machine learning classifiers, text embedding models, and DistilBERT, to establish baseline performance. Additionally, we propose a two-model approach to enhance humour style recognition, particularly in distinguishing between affiliative and aggressive styles. Our method demonstrates an 11.61% improvement in f1-score for affiliative humour classification, with consistent improvements in the 14 models tested. Our findings contribute to the computational analysis of humour in text, offering new tools for studying humour in literature, social media, and other textual sources.