  In traditional research approaches, sensory perception and emotion
classification have traditionally been considered separate domains. Yet, the
significant influence of sensory experiences on emotional responses is
undeniable. The natural language processing (NLP) community has often missed
the opportunity to merge sensory knowledge with emotion classification. To
address this gap, we propose SensoryT5, a neuro-cognitive approach that
integrates sensory information into the T5 (Text-to-Text Transfer Transformer)
model, designed specifically for fine-grained emotion classification. This
methodology incorporates sensory cues into the T5's attention mechanism,
enabling a harmonious balance between contextual understanding and sensory
awareness. The resulting model amplifies the richness of emotional
representations. In rigorous tests across various detailed emotion
classification datasets, SensoryT5 showcases improved performance, surpassing
both the foundational T5 model and current state-of-the-art works. Notably,
SensoryT5's success signifies a pivotal change in the NLP domain, highlighting
the potential influence of neuro-cognitive data in refining machine learning
models' emotional sensitivity.
