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A contextual-based approach for sarcasm detection.

Nivin A Helal1, Ahmed Hassan2, Nagwa L Badr1

  • 1Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt.

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|July 4, 2024
PubMed
Summary

This study enhances sarcasm detection by using contextual cues. Incorporating context significantly improved model performance, achieving up to 99% accuracy and reducing training time.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Sarcasm detection is challenging due to reliance on isolated utterances.
  • Contextual cues are crucial for accurately identifying sarcastic intent.

Purpose of the Study:

  • To develop an enhanced approach for sarcasm detection using contextual features.
  • To improve the accuracy and efficiency of identifying sarcasm in text.

Main Methods:

  • Utilized pre-trained transformer models: RoBERTa and DistilBERT.
  • Fine-tuned models on News Headlines and Mustard datasets with contextual information.
  • Experimented with context summarization to reduce training time.

Main Results:

  • Achieved F1 scores of 99% on News Headlines and 90% on Mustard datasets.
  • Context summarization reduced training time by 35.5%.
  • Sarcasm detection on Reddit dataset improved from 49% (without context) to 75% (with context).

Conclusions:

  • Contextual features significantly enhance sarcasm detection accuracy.
  • The proposed approach improves understanding of sarcasm in diverse settings.
  • Accurate sarcasm detection facilitates better sentiment analysis and decision-making.