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Published on: December 15, 2023
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Enhanced Semantic Representation Learning for Sarcasm Detection by Integrating Context-Aware Attention and Fusion
Shufeng Hao1,2, Jikun Yao3, Chongyang Shi4
1College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China.
Entropy (Basel, Switzerland)
|June 28, 2023
Summary
This study introduces a Contextual Sarcasm Detection Model (CSDM) to improve sarcasm detection on social media. The model enhances accuracy by incorporating user profiles and forum topics, outperforming existing methods.
Area of Science:
- Natural Language Processing
- Computational Linguistics
- Artificial Intelligence
Background:
- Sarcasm is common on social media, complicating sentiment analysis.
- Traditional methods focus on text content, neglecting contextual clues.
- Accurate sarcasm detection is crucial for understanding user sentiment.
Purpose of the Study:
- To develop an advanced model for detecting sarcasm in online text.
- To leverage contextual information beyond sentence content for improved accuracy.
Main Methods:
- Proposed a Contextual Sarcasm Detection Model (CSDM).
- Employed a Bi-LSTM encoder with context-aware attention for refined comment representation.
- Utilized a user-forum fusion network to integrate user profiling and forum topic information.
Main Results:
- CSDM achieved accuracies of 0.69, 0.70, and 0.83 on different datasets.
- Demonstrated significant performance improvement over state-of-the-art methods on a large Reddit corpus (SARC).
Conclusions:
- Contextual clues, including user profiles and forum topics, are vital for effective sarcasm detection.
- The proposed CSDM offers a more comprehensive approach to understanding online sentiment.
- This model advances the field of automatic sarcasm detection.
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