Enhancing Aspect Category Sentiment Analysis for Skier Satisfaction Evaluation: Leveraging Semantic Knowledge in Aspect Labels

Aspect category sentiment analysis (ACSA) has attracted considerable research interest for its advanced analysis capabilities; however, it confronts challenges such as limited datasets and data imbalances. In this work, we introduce a novel model that addresses data imbalance by leveraging semantic knowledge from aspect category labels, thus enabling more nuanced sentiment analysis. Our analysis demonstrated superior performance compared to benchmark models in comparative experiments conducted on the SemEval 2015 and 2016 restaurant datasets. More specifically, a 3.97% increase in accuracy and a 7.25% rise in the F-1 score for the 2015 dataset were achieved, alongside a 0.94% improvement in accuracy and a 2.41% increase in the F-1 score for the 2016 dataset. Additionally, we utilized a novel data augmentation (DA) approach, combining back-translation and GPT-3 augmentation, to enhance the dataset for skiers’ ACSA. This enhancement aims to improve our model’s predictive accuracy on skiers’ reviews. The experimental results showed that the proposed model, trained on the augmented training set, exhibited an average increase of 0.41% in accuracy and 1.78% in the F-1 score. Lastly, a case study at Nozawa Onsen ski resort using a novel approach combining ACSA and IPA demonstrated the approach’s effectiveness in identifying important aspects and gauging skier satisfaction. This study highlighted that significant attention should be paid to skiers’ dissatisfaction with aspects such as the crowdedness of the slopes at the resort.