Is GPT Powerful Enough to Analyze the Emotions of Memes?
Jingjing Wang Joshua Luo Grace Yang Allen Hong Feng Luo
Large Language Models (LLMs), representing a significant achievement in
artificial intelligence (AI) research, have demonstrated their ability in a
multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a
leading example of LLMs, in processing the sentiment analysis of Internet
memes. Memes, which include both verbal and visual aspects, act as a powerful
yet complex tool for expressing ideas and sentiments, demanding an
understanding of societal norms and cultural contexts. Notably, the detection
and moderation of hateful memes pose a significant challenge due to their
implicit offensive nature. This project investigates GPT's proficiency in such
subjective tasks, revealing its strengths and potential limitations. The tasks
include the classification of meme sentiment, determination of humor type, and
detection of implicit hate in memes. The performance evaluation, using datasets
from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative
understanding of GPT responses against human annotations. Despite GPT's
remarkable progress, our findings underscore the challenges faced by these
models in handling subjective tasks, which are rooted in their inherent
limitations including contextual understanding, interpretation of implicit
meanings, and data biases. This research contributes to the broader discourse
on the applicability of AI in handling complex, context-dependent tasks, and
offers valuable insights for future advancements.