Understanding emotions in text is crucial for creating truly intelligent AI. But current language models, while powerful, struggle with the nuances of sentiment, especially across different languages and complex contexts. Imagine an AI chatbot that completely misreads a customer's frustrated message or a translation tool that loses the emotional essence of a poem. These are the challenges researchers are tackling. A new framework called SentiXRL aims to boost AI's emotional quotient by enhancing how language models understand and classify emotions. SentiXRL utilizes two key innovations: an emotion retrieval enhancement module, which uses historical dialogue and logical reasoning to improve sentiment analysis accuracy, and a self-circulating analysis negotiation mechanism (SANM), enabling the AI to double-check its own work and refine its emotional understanding. Tested on several benchmark datasets in both English and Chinese, SentiXRL outperformed existing models. Notably, it excelled in deciphering complex textual environments, like social media posts, where information can be noisy and topics less defined. This research is not just about improving accuracy scores; it's about building AI that can genuinely connect with humans on an emotional level. Imagine AI tutors that can sense student frustration, customer service bots that offer genuine empathy, and content recommendation systems that understand your mood. While the focus is currently on textual data, the researchers plan to explore incorporating multimodal information like voice tone and facial expressions, moving us closer to AI that truly understands human emotions.