Extracting sensory information from text, particularly olfactory references, is challenging due to limited annotated datasets and the implicit, subjective nature of sensory experiences. This study investigates whether GPT-4o-generated data can complement or replace human annotations. We evaluate human- and LLM-labeled corpora on two tasks: coarse-grained detection of olfactory content and fine-grained sensory term extraction. Despite lexical variation, generated texts align well with real data in semantic and sensorimotor embedding spaces. Models trained on synthetic data perform strongly, especially in low-resource settings. Human annotations offer better recall by capturing implicit and diverse aspects of sensoriality, while GPT-4o annotations show higher precision through clearer pattern alignment. Data augmentation experiments confirm the utility of synthetic data, though trade-offs remain between label consistency and lexical diversity. These findings support using synthetic data to enhance sensory information mining when annotated data is limited.