CASD on enhancing sentiment analysis using context-aware sarcasm detection on social media

Effective sentiment analysis is crucial for understanding human language, especially when dealing with sarcasm. This study enhances sentiment analysis by integrating BERT contextual embeddings with an ensemble learning classifier to recognise sarcasm. A custom-labelled dataset rich in sarcastic elements was created to train and refine the model, utilising a random forest classifier for its robustness with complex datasets. Comparative analysis against standard models, including BOW-LR, VADER, SVM-TF-IDF, and LSTM, showed that the CASD model significantly improved performance metrics, achieving an accuracy of 0.85 and consistently outperforming baseline models across all sentiment classes. Notably, CASD achieved a precision score of 0.86 for neutral sentiment detection, illustrating its sensitivity to linguistic subtleties. This research introduces a novel framework that effectively accounts for sarcasm, leveraging BERT's contextual understanding and random forest's ensemble classification to advance sentiment analysis accuracy. This improvement is vital for applications requiring fine-grained sentiment detection, showcasing the potential for sophisticated natural language processing technologies to reflect the complexities of human communication more accurately.