Sentiment Analysis of YouTube Comments on K-Pop Music Videos Using Naïve Bayes: A Case Study of Jung Jaehyun's 'Horizon'
Sentiment Analysis of YouTube Comments on K-Pop Music Videos Using Naïve Bayes and SVM: A Case Study of Jung Jaehyun's 'Horizon'
DOI:
https://doi.org/10.21070/ups.6966Keywords:
sentiment analysis, naïve bayes, support vector machine, k-pop, youtube commentsAbstract
This study analyzes the sentiment of 2,391 Indonesian-language YouTube comments on Jung Jaehyun's Horizon music video using Naïve Bayes and Support Vector Machine (SVM) algorithms. As K-pop garners global attention, understanding fan sentiment through YouTube comments presents challenges due to unstructured and imbalanced data. After preprocessing data cleaning, tokenization, normalization, and stopword removal the comments were manually labeled as positive or negative. Naïve Bayes, known for its simplicity, and SVM with a linear kernel were compared using accuracy, precision, recall, and F1-score. SVM achieved 98% accuracy, excelling in handling imbalanced data, while Naïve Bayes delivered 97% accuracy, highlighting its efficiency with small datasets. Both algorithms struggled with the negative class, as shown in the confusion matrix. This research suggests Naïve Bayes for initial analysis and SVM for complex tasks, offering insights into fan sentiment and its role in assessing music industry success.
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