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Sarcasm detection framework using context, emotion and sentiment features
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DOI：10.1016/j.eswa.2023.121068.png)
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Abstract
En 中文
Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. In this paper, we propose a model, that incorporates different features to capture sarcasm. We use a pre-trained transformer and CNN to capture context features, and we use transformers pre-trained on emotions detection and sentiment analysis tasks. In our architecture, sentiment and emotion models were used only as feature extractors. Other blocks (pre-trained transformer and CNN) were fine-tuned. We run experiments on four datasets from different domains.Our approach outperformed previous state-of-the-art results on four datasets from social networking platforms and online media.
Keywords:
Sarcasm detection
Context
Emotion
Sentiment
Transformers
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