Multilingual sentiment analysis in restaurant reviews using aspect focused learning

Cross-cultural sentiment analysis in restaurant reviews presents unique challenges due to linguistic and cultural differences across regions. The purpose of this study is to develop a culturally adaptive sentiment analysis model that improves sentiment detection across multilingual restaurant reviews. This paper proposes XLM-RSA, a novel multilingual model based on XLM-RoBERTa with Aspect-Focused Attention, tailored for enhanced sentiment analysis across diverse cultural contexts. We evaluated XLM-RSA on three benchmark datasets: 10,000 Restaurant Reviews, Restaurant Reviews, and European Restaurant Reviews, achieving state-of-the-art performance across all datasets. XLM-RSA attained an accuracy of 91.9% on the Restaurant Reviews dataset, surpassing traditional models such as BERT (87.8%) and RoBERTa (88.5%). In addition to sentiment classification, we introduce an aspect-based attention mechanism to capture sentiment variations specific to key aspects like food, service, and ambiance, yielding aspect-level accuracy improvements. Furthermore, XLM-RSA demonstrated strong performance in detecting cultural sentiment shifts, with an accuracy of 85.4% on the European Restaurant Reviews dataset, showcasing its robustness to diverse linguistic and cultural expressions. An ablation study highlighted the significance of the Aspect-Focused Attention, where XLM-RSA with this enhancement achieved an F1-score of 91.5%, compared to 89.1% with a simple attention mechanism. These results affirm XLM-RSA's capacity for effective cross-cultural sentiment analysis, paving the way for more accurate sentiment-driven insights in globally distributed customer feedback.