Machine learning evaluation demonstrates high precision in multimodal sarcasm detection across benchmark datasets, indicating improved integration of text, video, and audio cues.
The rapid growth of online communication has increased the need for efficient multimodal sarcasm detection models. However, effectively integrating textual, visual, and audio information remains a significant challenge. To tackle this issue, this paper proposes a Conditional Random Field-assisted LinkNet framework (CRF-LN-MSD) that integrates aspect-level textual representations via a Modified Fisher Score-based Aspect Term Extraction (MFS-ATE) technique with facial expression features and audio spectral characteristics. The CRF-LN architecture employs a CRF layer for structured multimodal fusion and a novel Tanh-Swish-Sigmoid (TSS) activation function for improved gradient flow. Experimental results on the M2H2 and CMU-MOSI datasets demonstrate competitive performance under the evaluated experimental conditions, with a precision of 0.978 on M2H2 and a cross-validated accuracy of 0.9787 on CMU-MOSI. These results demonstrate the effectiveness of the proposed architectural refinement for multimodal sarcasm detection within the evaluated scope.
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Bavkar et al. (2026) studied this question.