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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
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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.

Keywords:
context-aware attentionfusion networkrepresentation learningsarcasm detection

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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.