Keywords:-

Keywords: Extreme Learning Machine, Weighted-ELM, Boosting Weighted-ELM, Imbalanced Data, Sentiment Analysis

Article Content:-

Abstract

This study compares three Extreme Learning Machine (ELM) variants: ELM, Weighted-ELM (WELM), and Boosting Weighted-ELM (BWELM) for sentiment analysis of user reviews from Indonesian railway applications. Using TF-IDF for feature extraction and IndoBERT for labeling, the models were evaluated on an imbalanced dataset. ELM achieved 68.25% accuracy but struggled with minority classes. WELM improved performance to 72% by addressing class imbalance. BWELM, combining WELM and AdaBoost, achieved the best result with 78.25% accuracy, effectively handling imbalanced data. The findings highlight BWELM's potential for sentiment analysis in real-world, imbalanced datasets.

References:-

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Yosephine, N., Warsito, B., & Nugraheni, D. M. (2025). A Comparative Study of Extreme Learning Machine Variants for Sentiment Analysis on Railway App Reviews. International Journal Of Mathematics And Computer Research, 13(7), 5359-5364. https://doi.org/10.47191/ijmcr/v13i7.01