A Transformer-Based Multi-Task Learning Framework for Sentiment, Emotion, and Sarcasm Analysis

A major challenge in natural language processing is determining the emotional intent of text because sentiment, emotion, and sarcasm all work together. Most contemporary methods deal with these tasks individually, which leads to incomplete representations and greater computational costs. This study provides a transformer-based multitask learning system that jointly performs sentiment classification, fine-grained emotion recognition, and sarcasm detection within a single architecture. The framework uses a shared RoBERTa-based encoder with task-specific classification heads to capture the contextual dependencies and subtle linguistic cues. Experimental results reveal that the proposed multitask technique delivers a robust and balanced performance across all tasks, highlighting its efficacy for comprehensive affective text analysis in real-world applications such as social media monitoring and customer feedback analysis.