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Hybrid Deep Learning for Meme Sentiment and Emotion Analysis Using LLMs

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
D. Swapna
M. Shanmuga Sundari
T. Nandini
S. K. Nyasa
M. Bhavya Bhavika
Published in
Computer Vision and Robotics
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the complexities of meme analysis, highlighting the challenges posed by the multimodal nature of memes. It explores the use of advanced AI technologies, including Large Language Models (LLMs) like GPT-4, to capture and classify memes based on their content, emotions, and intensity. The study introduces a framework that combines ResNet for image feature extraction, KeyBERT for keyphrase extraction, and UET for efficient feature representation. The experimental results demonstrate high accuracy in emotion detection, with a focus on reducing computational demands. The chapter also discusses the future of meme analysis, emphasizing the need for real-time analysis tools and the potential applications in public opinion understanding and content moderation.
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previous chapter Machine Learning-Driven Strategies for Laboratory Diagnostic Pathway Optimization
next chapter Advancements, Challenges, and Recent Trends in Facial Expression Recognition Systems: A Comprehensive Review
Title
Hybrid Deep Learning for Meme Sentiment and Emotion Analysis Using LLMs
Authors
D. Swapna
M. Shanmuga Sundari
T. Nandini
S. K. Nyasa
M. Bhavya Bhavika
Copyright Year
2026
Book
Computer Vision and Robotics

Print ISBN: 978-3-032-06252-9

Electronic ISBN: 978-3-032-06253-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-06253-6_29
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