Sentiment Analysis and Customer Satisfaction Prediction on E-Commerce Platforms Based on YouTube Comments Using the XGBoost Algorithm
Researchers applied XGBoost with TF-IDF vectorization to predict customer satisfaction from YouTube comments on Indonesian e-commerce videos, addressing the practical challenge of scaling sentiment analysis across unstructured social data. The work demonstrates how ensemble gradient-boosting methods remain effective for real-world NLP tasks when paired with classical feature engineering, relevant to practitioners building production sentiment systems that must operate on noisy, multi-lingual user-generated content without large labeled datasets.
MentionsXGBoost · TF-IDF · PyCaret · YouTube
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