Understanding sentiment is crucial for businesses and individuals alike. But can AI truly grasp the nuances of emotion across different languages? New research explores the abilities of large language models (LLMs) to perform aspect-based sentiment analysis (ABSA) in a multilingual setting—identifying not just the overall sentiment of a text, but the specific sentiment towards individual aspects, like the food or service at a restaurant. Researchers tested various LLMs, including open-source models like Llama and Mistral, and closed-source models like Gemini and GPT-4, using a range of prompting strategies. They discovered that while LLMs show promise, they currently lag behind traditional, fine-tuned models. Surprisingly, simpler prompts often outperformed complex reasoning techniques like chain-of-thought prompting. Performance also varied significantly across languages, likely reflecting the English-centric nature of current LLM training. This suggests that while LLMs are powerful tools, significant advancements are needed before they can truly understand the complexities of multilingual sentiment. Future research will focus on larger, more diverse datasets, fine-tuning strategies for multilingual ABSA, and language-specific prompt engineering to unlock the full potential of LLMs in this crucial area.