Open Access

ARTICLE

PKME-MLM: A Novel Multimodal Large Model for Sarcasm Detection

Jian Luo¹, Yaling Li¹, Xueyu Li¹, Xuliang Hu^2,*

1 College of Information Science and Engineering, Hunan Normal University, Changsha, 410000, China
2 Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410000, China

* Corresponding Author: Xuliang Hu. Email:

Computers, Materials & Continua 2025, 83(1), 877-896. https://doi.org/10.32604/cmc.2025.061401

Received 23 November 2024; Accepted 02 January 2025; Issue published 26 March 2025

Abstract

Sarcasm detection in Natural Language Processing (NLP) has become increasingly important, particularly with the rise of social media and non-textual emotional expressions, such as images. Existing methods often rely on separate image and text modalities, which may not fully utilize the information available from both sources. To address this limitation, we propose a novel multimodal large model, i.e., the PKME-MLM (Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection). The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images, which is then combined with text data for deeper analysis. This method improves the integration of image and text data, addressing the limitation of previous models that process these modalities separately. Additionally, we incorporate multi-label sentiment analysis, refining sentiment labels to improve sarcasm recognition accuracy. This design overcomes the limitations of prior models that treated sentiment classification as a single-label problem, thereby improving sarcasm recognition by distinguishing subtle emotional cues from the text. Experimental results demonstrate that our approach achieves significant performance improvements in multimodal sarcasm detection tasks, with an accuracy (Acc.) of 94.35%, and Macro-Average Precision and Recall reaching 93.92% and 94.21%, respectively. These results highlight the potential of multimodal models in improving sarcasm detection and suggest that further integration of modalities could advance future research. This work also paves the way for incorporating multimodal sentiment analysis into sarcasm detection.

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Keywords

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