A Two-Model Approach for Humour Style Recognition (2024)

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.

## introduction humour recognition is a multidimensional task influenced by various theories and manifested through diverse styles. there are various humour theories, such as relief, incongruity, and superiority theories @xcite @xcite . the relief theory highlights the role of humour in relaxation, while the incongruity theory suggests that we find something funny when we notice a mismatch or contradiction between what we expect in a situation and what actually happens. the superiority theory suggests that people may laugh at other people's misfortunes in an effort to demonstrate their superiority. these theories not only explain why we find things humorous but also why we laugh as a response. in recent decades, evolutionary psychology has introduced a new perspective on laughter itself, known as the play theory @xcite : laughter developed as a play signal in higher primates in their mock fights to indicate non-aggressive intent. laughter, therefore, is more than just a reaction to humour; it serves various functions, including promoting mental, emotional, and physical wellbeing. this idea forms the basis for laughter therapy, a cognitive-behavioural treatment designed to induce laughter and reduce stress, tension, anxiety, and sadness @xcite . however, as @xcite noted, not all humour is beneficial-some forms can even harm relationships with others or oneself. considering its impact on well-being, @xcite categorised humour into four styles: self-enhancing, self-deprecating, affiliative, and aggressive. affiliative and self-enhancing humour are beneficial to psychological well-being. affiliative humour fosters social bonding, while selfenhancing humour involves maintaining a positive outlook without harming oneself or others, often employed as a coping mechanism in difficult situations @xcite @xcite . in contrast, aggressive and self-deprecating humour can be harmful. aggressive humour, rooted in superiority theory, belittles or mocks others, whereas self-deprecating humour seeks approval by making oneself the target of jokes @xcite @xcite . in artificial intelligence (ai), humour is considered ai-complete @xcite @xcite , meaning that a system capable of producing and recognising human-like humour would possess general intelligence. despite the importance of humour, most computational efforts have focused on laughter @xcite @xcite , classification @xcite and generation @xcite , as well as humour detection @xcite @xcite , and humour generation @xcite @xcite , with little emphasis on humour styles and their links to well-being. @xcite identified a gap in the current ml landscape: the lack of datasets and models specifically designed to recognise these four humour styles. building on the gaps identified by @xcite , this study addresses the lack of an established dataset and ml models for recognising the four humour styles: self-enhancing, selfdeprecating, affiliative, and aggressive. we draw on @xcite , who defined and validated these styles, providing the theoretical basis for our classification task. additionally, edalat (2023)'s work on self-initiated humour protocols (sihp) informs how different humour styles can enhance well-being, while @xcite research on the link between humour styles, emotion regulation, and subjective well-being highlights the potential applications of our work in psychological and clinical contexts. by integrating these insights, we aim to develop a comprehensive approach to humour style recognition grounded in psychological theory and applicable to real-world scenarios. the key contributions of this paper are: humour recognition and classification are active research areas in nlp and multi-modal analysis. while our focus is on humour style recognition, we draw insights from related fields like general humour detection and sarcasm detection. weller and seppi (2020) compiled a dataset of 550,000 jokes from reddit posts, using user ratings and engagement metrics as quantifiable humour quality measurements. however, the dataset's reliance on reddit data alone may introduce biases and limit generalisability. our study addresses this by introducing a more diverse dataset specifically tailored for humour style recognition from various online sources. oliveira et al. (2020) explored humour recognition in portuguese text, achieving a 75% f1-score using naive bayes, support vector machine, and random forest classifiers. however, their work was limited to binary classification of headlines and one-liners. our approach extends this by focusing on multi-class classification of humour styles in both short and long texts. tang et al. (2022) created a dataset and classification model for sub-types of inappropriate humour, using large language models like bert. while relevant, their focus on inappropriate humour differs from our goal of recognising humour styles linked to psychological well-being. kamal and abulaish (2020) targeted selfdeprecating humour, one of the four styles we examine. their use of specific feature categories (self-deprecating pattern, and word embedding) informs our feature engineering process. however, our study broadens the scope to include all four humour styles. @xcite developed models for humour recognition in german football press conferences. although their work yielded promising results, it was limited to the muse humour challenge and the passau-sfch german dataset, unlike our broader approach. sarcasm detection is closely related to humour style recognition since it is often used in aggressive and self-deprecating humour styles. @xcite used an interactive graph convolution network for multi-modal sarcasm detection, highlighting the importance of contextual cues. this technique could be adapted to distinguish humour styles. jinks (2023) improved sarcasm detection with a two-step fine-tuning process using roberta, a method that could enhance humour style classification given the subtle differences between styles. @xcite introduces the single-stage extensive semantic fusion model for multi-modal sarcasm detection by concurrently processing and fusing multi-modal inputs in a unified framework. this approach could be adapted for humour style recognition, when we expand our dataset to include multi-modal features in the future. although these studies contribute to the detection of humour and sarcasm, there is a gap in recognising the four humour styles defined by @xcite . our work fills this gap by creating a dedicated dataset and developing classification models tailored to these humour styles. ## dataset collection and annotation a significant challenge in identifying humour styles automatically is the lack of annotated datasets suitable for training machine learning models. to address this, we created a comprehensive dataset comprising 1,463 instances from various sources: ## methodology this study employs two different approaches for humour style recognition: the single-model and the two-model approach. a total of 14 models were evaluated, including naive bayes, random forest, xgboost (each with six different text embeddings), and distilbert. figure 2 illustrates the two-model approach, which first classifies humour instances into broader groups before refining to specific styles. ## results and discussions experiments for the single-model and two-model approaches were conducted on fourteen models: nb, rf + six embedding models, xgboost + six embedding models and distilbert. ## conclusion automatic recognition of humour styles is a valuable yet challenging task with significant implications for digital humanities research, particularly in areas such as mental health, content moderation, and social media discourse. this study addresses the lack of established resources by introducing a new dataset of 1,463 instances across four humour styles and non-humour, while providing baseline evaluations of various models. our initial single-model approach struggled to accurately recognise affiliative humour, with f1scores ranging from 39.2% to 64.9%. to address this, we developed a two-model approach consisting of a four-class model (merging affiliative and aggressive styles) followed by a binary model dis-tinguishing between these styles. extensive evaluation demonstrated the effectiveness of this approach in improving affiliative humour recognition, achieving f1-scores of 50.5% to 66.1%, while maintaining good performance for other styles. furthermore, this approach offers flexibility in combining the best models for each sub-task, optimising overall performance. by introducing this dataset and baseline evaluations, we aim to catalyse further research and development in these critical areas of digital humanities, ultimately enhancing our understanding of humour and its multifaceted impact on human communication.