Application of TF-IDF and Xgboost Methods for Public Sentiment Analysis Towards Ozzaskin Skincare Brand on Social Media
DOI:
https://doi.org/10.55606/jeei.v5i1.3676Keywords:
Sentiment Analysis, Natural Language Processing (NLP), Social Media, Ozzaskin Skincare, Public OpinionAbstract
Ozzaskin is a local skincare brand founded by Ustadzah Oki Setiana Dewi that targets Muslim women and focuses on reducing dark spots and acne scars. Over time, this domestic brand has attracted considerable public attention on social media—particularly among mothers—garnering both praise for its product efficacy and criticism regarding price and texture. This study aims to analyze public sentiment toward the Ozzaskin brand by performing web scraping on Instagram and TikTok data, employing TF-IDF for textual feature extraction and XGBoost as the classification algorithm. The findings are expected to provide a comprehensive overview of consumer perceptions of Ozzaskin and to assist the marketing team and product developers in formulating communication strategies and improving product formulas that more effectively address user needs. The novelty of this research lies in the comprehensive application of the TF-IDF + XGBoost framework for brand-related sentiment analysis on Indonesian-language social media.
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