Sentiment analysis is a natural language processing (NLP) technique that determines the emotional tone of speech. This strategy enables businesses and scholars to study public sentiment and make data-driven judgments. Traditionally, sentiment analysis has used either machine learning (ML) or deep learning (DL) models, each with its own set of advantages and disadvantages. ML models often excel in interpretability but may lack the understanding of context and complexity that DL models offer. Particularly when considering language nuances like sarcasm and idioms, our results expose a notable discrepancy in the capacity of current models to create a harmonic balance between precision and comprehensibility. Many present models lack a comprehensive justification for their classifications, which is essential for uses requiring a comprehensive knowledge of the variables generating sentiment. A new model combining deep learning’s analytical depth with machine learning’s simplicity and clarity was developed to meet these issues. A new model was developed to solve these issues combining the simplicity and clarity of machine learning with the analytical depth of deep learning. This method lets users grasp the fundamental concepts guiding every sentiment prediction, hence enhancing the comprehensibility and accuracy of sentiment classification.Its test ROC-AUC was 92.87% and its train ROC-AUC was 99.87%. Especially in connection to complex linguistic factors, our results outperform traditional models and provide perceptive sentiment analysis analysis. This model shows a considerable development in sentiment analysis with remarkable accuracy and enhanced understandability.