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Bayu Wibisono, Gunthur
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Computer Science & IT

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Journal : Nuansa Informatika

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Emoji-Based Sentiment Classification Using Ensemble Learning with Cross-Validation: A Lightweight Approach for Social Media Analysis: Klasifikasi Sentimen Berbasis Emoji Menggunakan Ensemble Learning dengan Validasi Silang: Pendekatan Ringan untuk Analisis Media Sosial Alamsyah, Nur; Bayu Wibisono, Gunthur; Parama Yoga, Titan; Budiman; Hendra, Acep
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.396

Abstract

The increasing use of emojis in online communication reflects emotional expression that is often more immediate and intuitive than text. This study proposes a lightweight sentiment classification approach that utilizes only emoji features extracted from social media posts, without relying on textual content. The importance of this research lies in its relevance to short-form digital content, where textual sentiment cues are minimal or absent. To address the classification problem, we implement and compare multiple machine learning models including Random Forest (RF), Support Vector Machine, and an ensemble Voting Classifier combining both. Emoji tokens were vectorized using character-level count vectorization, and performance was evaluated using 5-fold cross-validation to ensure robustness and generalizability. Results show that the ensemble model achieved the highest average accuracy of 93.6%, outperforming the individual classifiers. These findings confirm that emojis alone can serve as reliable indicators of sentiment and support the deployment of fast, interpretable, and scalable models for social media sentiment analysis.
Co-Authors Acep Hendra Budiman Nur Alamsyah PARAMA YOGA, TITAN