Detecting sarcasm in user-generated content integrating transformers and gated graph neural networks.

The widespread use of the Internet and social media has posed significant challenges to automated sentiment analysis, particularly in relation to detecting sarcasm in user-generated content. Sarcasm often expresses negative emotions through seemingly positive or exaggerated language, making its detection a complex task in natural language processing. To address this issue, the present study proposes a novel sarcasm detection model that combines bidirectional encoder representations from transformers (BERT) with gated graph neural networks (GGNN), further enhanced by a self-attention mechanism to more effectively capture ironic cues. BERT is utilized to extract deep contextual information from the text, while GGNN is employed to learn global semantic structures by incorporating dependency and emotion graphs. Experiments were conducted on two benchmark sarcasm detection datasets, namely Headlines and Riloff. The experimental results demonstrate that the proposed BERT-GGNN model achieves an accuracy of 92.00% and an F1 score of 91.51% on the Headlines dataset, as well as an accuracy of 86.49% and an F1 score of 86.59% on the Riloff dataset, significantly outperforming the conventional BERT-GCN models. The results of ablation studies further corroborate the efficacy of integrating GGNN, particularly for handling complex ironic expressions frequently encountered in social media contexts.