Research on Sarcastic Emotion Recognition Based on Multiple Feature Fusion
Sarcasm detection significantly enhances the performance of various natural language processing applications, such as sentiment analysis, opinion mining, and stance detection. Despite considerable advancements in this field, research results remain fragmented across diverse datasets and studies. Thi...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02008.pdf |
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| Summary: | Sarcasm detection significantly enhances the performance of various natural language processing applications, such as sentiment analysis, opinion mining, and stance detection. Despite considerable advancements in this field, research results remain fragmented across diverse datasets and studies. This paper offers a critical review of two predominant models in sarcasm detection. The first model utilizes BERT within an intermediate task transfer learning framework, leveraging the connection between sarcasm and underlying negative emotions and sentiments. This model enhances the sarcasm detection capability through a strategic knowledge infusion into the transfer learning process. The second model reviewed deploys a multi-head attention-based bidirectional LSTM architecture. This approach incorporates pre-trained word embeddings, multi-head attention mechanisms, and custom-crafted features to proficiently identify sarcasm in social media datasets. Comparative assessments on standard datasets reveal that both models achieve superior performance over many existing approaches in the field. At last, this paper explores the direction for future improvement based on the conclusions. |
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| ISSN: | 2271-2097 |