sentiment-classification

The paper proposes QTrans, a quantum-classical hybrid transformer model for sentiment classification that uses parameterized quantum circuits for attention, achieving improved accuracy over classical baselines on benchmark datasets.

The paper proposes a multimodal solution for audio sentiment polarity classification that integrates audio and multilingual text transcripts via cross-modal transformers, and uses knowledge distillation to enhance an audio-only model without computational overhead during inference.

A comparative study evaluating three explainability techniques (Integrated Gradients, Attention Rollout, SHAP) on fine-tuned DistilBERT for sentiment classification, highlighting trade-offs between gradient-based, attention-based, and model-agnostic approaches for LLM interpretability.