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Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning.

Yik Yang Tan1, Chee-Onn Chow1, Jeevan Kanesan1

  • 1Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.

Wireless Personal Communications
|March 29, 2023
PubMed
Summary
This summary is machine-generated.

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This study shows that sentiment analysis and sarcasm detection are linked. A new deep learning model improves sentiment analysis by jointly learning both tasks, achieving a 94% F1-score.

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Social media generates vast amounts of user-generated content valuable for sentiment analysis.
  • Sentiment analysis classifies text as positive, negative, or neutral, but struggles with sarcasm.
  • Sarcasm, often with negative intent masked by positive language, is a challenge for sentiment analysis.

Purpose of the Study:

  • To investigate the correlation between sentiment analysis and sarcasm detection.
  • To develop a unified framework that leverages this correlation to enhance sentiment analysis performance.
  • To address the limitations of standalone sentiment and sarcasm classification models.

Main Methods:

  • Proposed a multi-task learning framework using a deep neural network.
Keywords:
Deep learning algorithmMulti-task learningSarcasm detectionSentiment analysis

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  • Modeled the inherent correlation between sentiment analysis and sarcasm detection tasks.
  • Utilized deep learning algorithms for improved text categorization.

    • Main Results:

    • Demonstrated a significant correlation between sentiment analysis and sarcasm detection.
    • The proposed multi-task learning model achieved a 94% F1-score.
    • Outperformed existing standalone methods by 3%.

    Conclusions:

    • Jointly learning sentiment analysis and sarcasm detection improves overall performance.
    • The developed deep learning framework effectively models the relationship between the two tasks.
    • This approach offers a more robust solution for analyzing sentiment in text data, especially on social media.