SinoSarcasmClassifier: A Multi-View Model for Sarcasm Detection in Chinese Social Media with Emoji Mapping

Detecting sarcasm in Chinese texts presents unique challenges due to its indirect expression, which complicates accurate identification. Inaccurate assessments of sarcastic content on social media platforms often lead to negative user interactions, highlighting the importance of precise sarcasm detection. Emojis, which are widely used on Chinese social networking platforms such as Sina Weibo (hereafter Weibo), a service similar to X (formerly Twitter) and Facebook, add additional layers to textual communication, conveying emotions, intentions, and potentially sarcasm. However, the lack of well-annotated, high-quality Chinese datasets poses a significant obstacle to effective sarcasm detection, while the contextual complexity of Chinese sarcasm remains a major challenge for current language models. To address these issues, we propose a method that integrates four distinct modules to achieve comprehensive sarcasm detection in Chinese social media comments, particularly short user-generated texts with emoji interactions. Our model leverages emojis as a critical feature and capitalizes on the structural and lexical characteristics of Chinese sarcastic sentences. By incorporating features from both emoji-enhanced and plain text representations, the model demonstrates significantly improved accuracy in detecting sarcasm. Additionally, to support the training, testing, and validation of the system, we constructed a carefully designed dataset comprising multiple subsets. These subsets not only support the model’s training and evaluation but also serve as valuable resources for future research on Chinese sarcasm detection.