Holistic Visualization of Contextual Knowledge in Hotel Customer Reviews Using Self-Attention

Traditional methods for analyzing customer reviews, such as word clouds and sentiment analysis, often fall short in providing comprehensive insights for business decision-making. Recent advancements in natural language processing (NLP) have improved interpretability, yet existing approaches struggle to simultaneously extract practically significant opinions, preserve contextual information, and offer intuitive visualizations of overall trends. This study proposes a novel visualization method for key opinion extraction that addresses these challenges by integrating self-attention models with dependency relationships. Our approach combines attention visualization with semantic relationships between words, enabling a holistic view of customer opinions while maintaining individual review context. We demonstrate the practical utility of our method using customer review data from a major Japanese hotel search site, revealing significant factors influencing both low and high hotel ratings. The results highlight our method’s effectiveness in extracting actionable insights from customer reviews, offering valuable implications for the hospitality industry and beyond.