I’m Training a Sentiment Analysis Model, but It Fails on Sarcasm—How Can I Make It More Context-Aware? #848
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I’m building an NLP model for sentiment analysis using a BERT-based architecture, and while it performs well overall (~90% accuracy), it consistently fails to detect sarcastic or ironic statements. This leads to misclassification, especially in social media data. I’ve tried fine-tuning on general sentiment datasets, but it doesn’t seem enough. Should I incorporate additional features like emoji usage, punctuation, or user metadata? Are there sarcasm-specific datasets or modeling techniques that can help improve performance? |
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Sarcasm detection is a known challenge in sentiment analysis due to its reliance on nuanced context. To improve performance, fine-tune your model on sarcasm-specific datasets like the SARC dataset or Twitter sarcasm corpora. Incorporating multimodal features such as emojis, punctuation patterns, and even user behavior metadata can enhance contextual understanding. Adding a separate sarcasm detection head in a multi-task learning setup is also effective. Recent approaches using attention-based models or combining BERT with sentiment-aware transformers have shown up to a 10–15% boost in sarcasm detection accuracy. |
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Sarcasm detection is a known challenge in sentiment analysis due to its reliance on nuanced context. To improve performance, fine-tune your model on sarcasm-specific datasets like the SARC dataset or Twitter sarcasm corpora. Incorporating multimodal features such as emojis, punctuation patterns, and even user behavior metadata can enhance contextual understanding. Adding a separate sarcasm detection head in a multi-task learning setup is also effective. Recent approaches using attention-based models or combining BERT with sentiment-aware transformers have shown up to a 10–15% boost in sarcasm detection accuracy.