Sentiment Analysis of K-pop Fans Toward NCT Concerts on X (Twitter) Using the Transformer Model XLM-RoBERTa
DOI:
https://doi.org/10.30871/jaic.v10i2.12333Keywords:
Sentiment Analisis, X (Twitter), K-pop Concert, Transformer, XLM-RoBERTaAbstract
The large volume of unstructured tweets makes manual sentiment analysis inefficient and prone to bias, therefore, an automatic sentiment classification approach using Natural Language Processing (NLP) is required. Tweet data were collected through data crawling using Tweet Harvest during the period of January 2025 to November 2025. Sentiment classification was performed using a pre-trained XLM-RoBERTa Transformer model through inference without additional fine-tuning, producing sentiment labels (positive, neutral, and negative) along with probability scores. The results indicate that positive sentiment dominates discussions about the TDS concert, reflecting fans generally enthusiastic and favorable responses. Neutral tweets mainly contain informational content, while negative tweets are commonly related to ticket scams and technical issues. Overall, this study demonstrates that XLM-RoBERTa is effective in performing contextual sentiment classification to capture K-pop fans responses toward concert events on social media.
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