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

TL;DR

This paper develops a machine learning model using XGBoost and TF-IDF to predict customer satisfaction from YouTube comments on e-commerce videos, highlighting the influence of socio-political language.

Contribution

It introduces a novel approach combining XGBoost and text preprocessing for sentiment analysis on YouTube comments related to e-commerce, with insights into socio-political language impact.

Findings

PyCaret-optimized framework achieves high classification resilience.

Socio-political terms significantly influence comment polarity.

Lexical analysis reveals language patterns affecting satisfaction.

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.