Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications

2024-01-22

arXiv.org

AI Summary

This work addresses the semantic ambiguity of emojis in social media, alongside the high cost and subjectivity of manual annotation, by proposing the first large language model (LLM)-based framework for interpretable emoji understanding. Methodologically, it systematically evaluates ChatGPT (GPT-3.5 and GPT-4) on emoji semantic parsing, sentiment classification, and intent inference under zero-shot and few-shot prompting, and assesses cross-task generalization. Key contributions include: (1) the first empirical demonstration that ChatGPT achieves human-level or near-human performance across multiple emoji understanding tasks; (2) substantial reduction in annotation cost while improving consistency and transparency of semantic interpretation; and (3) a human-AI collaborative, explainable framework that mitigates discrepancies arising from subjective user interpretations. The results establish a new paradigm for social text analysis—efficient, robust, and auditable—grounded in LLM-driven interpretability.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Emotional Intelligence

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Emojis, which encapsulate semantics beyond mere words or phrases, have become prevalent in social network communications. This has spurred increasing scholarly interest in exploring their attributes and functionalities. However, emoji-related research and application face two primary challenges. First, researchers typically rely on crowd-sourcing to annotate emojis in order to understand their sentiments, usage intentions, and semantic meanings. Second, subjective interpretations by users can often lead to misunderstandings of emojis and cause the communication barrier. Large Language Models (LLMs) have achieved significant success in various annotation tasks, with ChatGPT demonstrating expertise across multiple domains. In our study, we assess ChatGPT's effectiveness in handling previously annotated and downstream tasks. Our objective is to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications. Our findings indicate that ChatGPT has extensive knowledge of emojis. It is adept at elucidating the meaning of emojis across various application scenarios and demonstrates the potential to replace human annotators in a range of tasks.

Problem

• Challenges in annotating emojis via crowd-sourcing methods
• Misunderstandings due to subjective emoji interpretations
• Need for automated emoji analysis in social media

Innovation

• Using ChatGPT for emoji sentiment analysis
• Replacing human annotators with ChatGPT
• Enhancing emoji meaning clarity online