Recent advancements in sentiment analysis have been driven by deep learning techniques and the use of pre-trained language models, which have enhanced the ability to interpret textual data more effectively. However, significant challenges remain in accurately understanding nuanced emotions and adapting sentiment analysis models to varying contexts. These difficulties are primarily attributed to insufficient semantic grounding and limited generalization capabilities within current models. The lack of a robust semantic foundation hinders the models' capacity to fully grasp the subtleties of human emotions and contextual variations. Research highlights that while deep learning improves overall sentiment analysis performance, addressing semantic grounding is crucial to overcoming persistent obstacles. This ongoing work underscores the importance of integrating deeper semantic understanding to refine sentiment analysis further. Such insights align with broader trends in artificial intelligence research focused on enhancing language comprehension and contextual adaptability.