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XGBoost algorithm predicts e-commerce customer satisfaction from YouTube comments

By PulseAugur Editorial · [2 sources] ·

This research paper introduces a predictive model for customer satisfaction using the XGBoost algorithm and TF-IDF vectorization on YouTube comments from Indonesian e-commerce review videos. The study found that the PyCaret-optimized machine learning framework achieved superior classification performance. Notably, the analysis revealed that socio-political terms frequently appear in e-commerce discussions, impacting customer sentiment. AI

IMPACT This study highlights how socio-political discourse can influence customer satisfaction predictions in e-commerce, suggesting a need for more nuanced sentiment analysis.

RANK_REASON This is a research paper published on arXiv detailing a new predictive model.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

XGBoost algorithm predicts e-commerce customer satisfaction from YouTube comments

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This is a research paper published on arXiv detailing a new predictive model.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Ridho Benedictus Togi Manik, Muhammad Aqil Ramadhan, Ihsan Maulana Yusuf, Luluk Muthoharoh, Ardika Satria, Martin Clinton Tosima Manullang ·

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

    arXiv:2605.04887v1 Announce Type: new 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 …

  2. arXiv cs.CL TIER_1 English(EN) · Martin Clinton Tosima Manullang ·

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

    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…

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