An Ensemble Machine Learning Approach for Sentiment Analysis of Roblox Reviews on Google Play Store
Pendekatan Ensemble Machine Learning untuk Analisis Sentimen Ulasan Roblox di Google Play Store
DOI:
https://doi.org/10.21070/ups.11420Keywords:
analisis sentimen, ulasan Roblox, ensemble learning, majority voting, machine learningAbstract
This study aims to classify user sentiment toward Roblox reviews on the Google Play Store using an ensemble machine learning approach. The initial dataset comprised 11,195 reviews collected using Google Play Scraper. After preprocessing and duplicate removal, 10,937 reviews were retained and automatically labeled as positive, neutral, or negative using InSet Lexicon. The classification process applied TF-IDF feature extraction, SMOTE–Tomek Links for balancing training data, and four algorithms: Naive Bayes, Support Vector Machine, K-Nearest Neighbors, and Random Forest. Negative sentiment dominated the dataset at 40.50%, followed by positive sentiment at 31.57% and neutral sentiment at 27.92%. Support Vector Machine achieved the best individual performance with 93.69% accuracy. Ensemble Majority Voting produced the highest result on the 90:10 split, reaching 93.97% accuracy and a 93.96% F1-score. These findings show that ensemble learning can improve sentiment classification performance for Roblox user reviews.
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