講演情報
[1A-04]Detecting Sarcasm Comments in Chinese SNS Through Emoji and Language Model Integration
*劉 子沛1、前田 亮2 (1. 立命館大学情報理工学研究科、2. 立命館大学情報理工学部)
発表者区分：学生
論文種別：ショートペーパー
インタラクティブ発表：あり
論文種別：ショートペーパー
インタラクティブ発表：あり
キーワード：
Sarcasm detection、Emojis interpretation、Social media analysis、NLP (Natural Language Processing)、BERT
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, widely used in Chinese social networking services (SNS) such as Sina Weibo, 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 texts. 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.
To address these issues, we propose a method that integrates four distinct modules to achieve comprehensive sarcasm detection in Chinese texts. 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.