VADER-RLA: A REINFORCEMENT LEARNING-AUGMENTED SENTIMENT ANALYSIS MODEL LEVERAGING VADER FOR CONTEXT-AWARE EMOTION CLASSIFICATION

VADER-RLA is presented as a hybrid architecture that utilizes reinforcement learning through VADER, expanding the contextual applicability of already established sentiment scoring in favour of comprehensive emotion classification, demonstrating that this framework can bridge the gap of ability to properly predict sentiment in tandem with trustworthiness. The most novel aspect of this work is the application of reinforcement learning to the problem of adaptive sentiment classification, hence allowing the model to perform real-time optimization of the sentiment scoring in changing linguistic surroundings. Proposed approach overcomes lexicon-based models and deep learning models, with respect to adaptability, precision and interpretability. Results show that VADER-RLA consistently exceeds traditional methods, yielding impressive accuracy and robustness and successfully detecting sophisticate sentiments like sarcastic and mixed ones. The experimental results indicate that VADER-RLA achieved an accuracy of 92.3%, an F1-score of 0.89, precision of 91.7%, and recall of 90.5%, demonstrating significant improvements over the baseline VADER model. These findings highlight the potential of VADER-RLA to provide a robust, adaptive solution for sentiment analysis in environments with rapidly changing linguistic trends.