A Comparative Study of NLP-Based Models: Popularity Trends in Pop Mart Comments

Abstract

In today's digital era, where consumer sentiment significantly influences brand perception, understanding customer emotions has become a strategic priority for companies like POP MART. Among its iconic collectibles, Labubu stands out as a symbol of the brand's global appeal, gaining tremendous popularity through the viral influence of Lisa from BLACKPINK, who showcased her Labubu collection on social media. This moment sparked a surge in interest, especially in Thailand, where Labubu achieved over 365,000 mentions in a single month, driven by its appeal to young, trend-conscious consumers. This study, Sentiment Analysis of LABUBU Popularity Trends: A Study of POPMART Comments Using NLP Techniques, investigates customer perceptions and emotions toward Labubu by employing advanced transformer-based NLP models - WangchanBERTa, RoBERTa, and XLM-R. By analyzing Thai-specific and multilingual datasets collected from social media platforms, this research evaluates the models' performance in sentiment classification. The analysis highlights WangchanBERTa's superior performance on Thai-specific datasets, achieving the highest F1-score of 87.51%. XLM-R demonstrated robust multilingual capabilities with an F1-score of 84.65%, while RoBERTa excelled in English sentiment analysis with an F1-score of 82.83%. These findings emphasize the importance of leveraging both language-specific and multilingual models to address cultural and linguistic nuances effectively. Sentiment patterns reveal positive comments highlighting emotional connections to Labubu, neutral sentiments about logistical inquiries, and negative feedback on technical and availability issues. These insights guide strategies to enhance marketing, boost customer engagement, and address operational challenges. This study advances NLP applications in sentiment analysis, offering POP MART data-driven insights to refine global and regional strategies while underscoring the value of multilingual transformer models in analyzing diverse consumer feedback.