Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition (2025)

Abstract: Humor recognition aims to identify whether a specific speaker’s text is humorous. Current methods for humor recognition mainly suffer from two limitations: (1) they solely focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor; and (2) they typically overlook the critical role of speaker individuality, which is essential for a comprehensive understanding of humor expressions. To bridge these gaps, we introduce the Commonality and Individuality Incorporated Network for Humor Recognition (CIHR), a novel model designed to enhance humor recognition by integrating multifaceted humor commonalities with the distinctive individuality of speakers. The CIHR features a Humor Commonality Analysis module that explores various perspectives of multifaceted humor commonality within user texts, and a Speaker Individuality Extraction module that captures both static and dynamic aspects of a speaker’s profile to accurately model their distinctive individuality. Additionally, Static and Dynamic Fusion modules are introduced to effectively incorporate the humor commonality with speaker’s individuality in the humor recognition process. Extensive experiments demonstrate the effectiveness of CIHR, underscoring the importance of concurrently addressing both multifaceted humor commonality and distinctive speaker individuality in humor recognition.

## introduction humor is a vital component of human communication and is crucial in the pursuit of creating human-like artificial intelligence. as the basis for machines to understand and respond to humor, humor recognition has been a challenging problem for researchers because of its inherent subjectivity, fuzziness and semantic complexity @xcite . despite these obstacles, the * corresponding author i used to be a baker, but i couldn't make enough dough. i wanted to be a banker, but i lost interest. ## related work early humor recognition research predominantly utilized feature engineering and traditional machine learning techniques to identify linguistic and stylistic features of humorous texts @xcite . for example, mihalcea and strapparava (2005) employed features such as alliteration, antonyms, and adult slang to model humor, using classifiers like naive bayes (nb) and support vector machines (svm) @xcite for humor recognition. @xcite explored grammatical, syntactic, semantic, and pragmatic features of humor texts, enriching the field of feature-based humor recognition. liu et al. ( 2018 ) combined emotional and semantic analysis to model the emotional aspects of humor. while effective in some scenarios, these methods significantly struggle to capture the deep semantic and contextual nuances vital for understanding humor. additionally, their reliance on manually designed features, which are labor-intensive to create and inflexible in application, further limits their effectiveness. the advent of deep learning marked a significant shift towards using neural networks for automatic feature extraction. additionally, the introduction of transformer-based pretrained language models significantly enhanced the performance of humor recognition systems. for instance, chen and lee (2017) developed a cnn-based model to recognize humor in speeches and puns, outperforming traditional methods even without manually designed features. @xcite introduced the transformer model, which further improved the ability to learn complex humor patterns and exhibited superior performance compared to other deep neural networks. @xcite utilized gpt @xcite to calculate the inconsistency between punchlines in jokes, enhancing humor recognition based on these inconsistency scores. @xcite used pretrained bert (kenton and toutanova, 2019) to obtain sentence embeddings for humor recognition. @xcite leveraged social network information and graph neural networks to enhance humor recognition results. despite significant progress in humor recognition research, recent approaches tend to focus primarily on single semantic layers, struggling to analyze user language from the diverse common perspectives of humor expression. additionally, a critical limitation of these methods is their neglect of the speaker's individual expression styles. ## problem statement the objective of humor recognition is to determine whether a specific speaker's text x is humorous or humorless. formally, for a given speaker's text x, the humor recognition model @xmath0. as our approach aims to analyze user humor from both the perspectives of humor commonalities and speaker individuality, it is essential to incorporate additional user profile information. accordingly, our inputs consist of the user text x and the user profile p x . therefore, our humor recognition model is expressed as @xmath1, p x ), where the user profile p @xmath2, d x } encompasses both static and dynamic aspects of the user profile. the static profile s x contains stable attributes such as the user's gender, age, region, and personal descriptions. conversely, the dynamic profile d x consists of the user's historical posts that are regularly updated. ## methodology the overall architecture of cihr is illustrated in figure 2 . cihr is structured around four main components: humor commonality analysis module, speaker individuality extraction module, static fu-sion module, and dynamic fusion module. the humor commonality analysis (hc) module leverages the powerful text interpretation capabilities of large language models to perform multiperspective humor commonalities analyses for user texts. the speaker individuality extraction (si) module extracts both static and dynamic profiles from user profiles and embeds them to model the speaker's individuality with greater detail and accuracy. the static fusion (sf) module introduces profile guided self-attention (pg-sa) and profile guided layer normalization (pg-ln) to integrate information from the user's static profile into the text modeling process. as a result, through the sf, cihr obtains a static profile guided multiperspective humor text representation and a static profile guided dynamic profile representation. the dynamic fusion (df) module leverages attention mechanisms and gated neural networks to produce a final text representation that effectively merges perspectives of commonality and individuality. finally, cihr employs this dual-perspective text representation to determine whether user text is humorous. detailed descriptions of each component follows in subsequent sections. ## overall performance as shown in ## conclusion this study introduced the commonalities and individuality incorporated network for humor recognition (cihr), an innovative approach addressing the limitations of existing models by incorporating humor commonalities with speaker individuality, aspects often neglected in traditional humor recognition. cihr features a humor commonality analysis module to explore diverse aspects of multifaceted humor commonality within user texts, complemented by a speaker individuality extraction module to capture unique individuality of each speaker. the integration of these insights is enhanced through the static and dynamic fusion modules, which effectively combine these elements to deepen the understanding of humor. experimental results validate cihr's effectiveness, showcasing significant improvements over existing models in terms of accuracy, precision, recall, and f1 scores. these findings emphasize the crucial importance of simultaneously considering both multifaceted humor commonality and distinctive speaker individuality to achieve robust and effective humor recognition.