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Memes have become a dominant form of digital communication, blending images and text to convey emotions, humor, sarcasm, or even controversial viewpoints. While memes enhance online engagement, they also pose risks by spreading hate speech and inciting violence. To address this, we utilize Artificial Intelligence (AI) to analyze and classify meme emotions. Our approach employs GPT-4 for deep contextual understanding, enabling precise sentiment categorization. The implemented model identifies and quantifies six emotional categories—happy (0), sad (0), angry (0), surprised (2), neutral (2), fearful (2), and disgusted (1)—based on meme content analysis. The results demonstrate the effectiveness of AI in detecting nuanced emotional cues, aiding in the development of responsible content moderation systems. This research enhances digital safety while preserving the expressive nature of memes through data-driven sentiment classification.

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Hybrid Deep Learning for Meme Sentiment and Emotion Analysis Using LLMs

  • 会议论文

  • 发表日期：

    2026年

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  • D. Swapna,
  • M. Shanmuga Sundari,
  • T. Nandini,
  • S. K. Nyasa,
  • M. Bhavya Bhavika

摘要

Memes have become a dominant form of digital communication, blending images and text to convey emotions, humor, sarcasm, or even controversial viewpoints. While memes enhance online engagement, they also pose risks by spreading hate speech and inciting violence. To address this, we utilize Artificial Intelligence (AI) to analyze and classify meme emotions. Our approach employs GPT-4 for deep contextual understanding, enabling precise sentiment categorization. The implemented model identifies and quantifies six emotional categories—happy (0), sad (0), angry (0), surprised (2), neutral (2), fearful (2), and disgusted (1)—based on meme content analysis. The results demonstrate the effectiveness of AI in detecting nuanced emotional cues, aiding in the development of responsible content moderation systems. This research enhances digital safety while preserving the expressive nature of memes through data-driven sentiment classification.