Sentiment Analysis and Customer Satisfaction Prediction on E-Commerce Platforms Based on YouTube Comments Using the XGBoost Algorithm

• URL: http://arxiv.org/abs/2605.04887v1
• Date: Wed, 06 May 2026 13:20:58 GMT
• Title: Sentiment Analysis and Customer Satisfaction Prediction on E-Commerce Platforms Based on YouTube Comments Using the XGBoost Algorithm
• Abstract summary: The exponential expansion of digital commerce in Indonesia has significantly shifted consumer interactions toward video-centric social networks.<n>This study investigates and constructs a predictive model for customer satisfaction leveraging the Extreme Gradient Boosting architecture.<n>The experimental results demonstrate that the PyCaret-optimized machine learning framework delivers superior classification resilience.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exponential expansion of digital commerce in Indonesia has significantly shifted consumer interactions toward video-centric social networks, particularly YouTube. Consequently, the sheer volume of unstructured, multi-contextual comments poses a tremendous challenge for manual sentiment tracking. This study investigates and constructs a predictive model for customer satisfaction leveraging the Extreme Gradient Boosting (XGBoost) architecture coupled with Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. By utilizing a secondary dataset of YouTube comments retrieved from e-commerce review videos, the raw text underwent rigorous preprocessing to generate normalized numerical features. The experimental results demonstrate that the PyCaret-optimized machine learning framework delivers superior classification resilience. Beyond standard performance metrics, lexical evaluations and feature-importance mapping uncover a notable phenomenon: e-commerce discourse is heavily infiltrated by socio-political terminologies, which ultimately influence the polarity of audience satisfaction.

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