Enhancing Chinese Sarcasm Detection with A Large-Scale Manually Annotated Dataset and A Hybrid Deep Learning Model

Sarcasm detection is a critical aspect of sentiment analysis on social media platforms, where expressions often convey meanings that deviate from their literal interpretations. Although Chinese is considered a high-resource language, detecting sarcasm remains challenging due to the absence of large-scale, annotated datasets and sarcasm’s complex, context-dependent, and culturally nuanced nature. This paper presents a multi-dimensional approach to sarcasm detection in Chinese, effectively addressing these challenges. We introduce Chinese TikTok Sarcasm Dataset (CTSD), a relatively large-scale, manually annotated collection of sarcastic comments sourced from TikTok, to address data scarcity. We demonstrate the effectiveness of the new dataset through extensive experiments using state-of-the-art models and introduce a hybrid deep learning model, BERTWWM-CNN-BiLSTM, which combines the power of Bidirectional Encoder Representations from Transformers with whole word masking (BERT-wwm) for rich contextual embeddings, Convolutional Neural Network (CNN) to capture local n-gram features, and Bidirectional Long Short-Term Memory network (BiLSTM) for learning long-range dependencies. Experimental results show that BERTWWM-CNN-BiLSTM outperforms baseline models, achieving an F1-score of 79.07% and an Area Under the ROC Curve (AUC) of 85.51%, reflecting improvements of 3.58% and 10.63%, respectively. These findings highlight the potential of our approach in enhancing sarcasm detection in social media text.