Hybrid TF--IDF Logistic Regression and MLP Neural Baseline for Indonesian Three-Class Sentiment Analysis on Social Media Text
Researchers benchmarked classical and neural baselines for Indonesian sentiment classification, combining TF-IDF with logistic regression and a shallow MLP on a 707-sample dataset remapped to three classes. The work addresses a persistent gap in non-English NLP evaluation, where resource-constrained settings and imbalanced data remain common obstacles. While methodologically straightforward, the study contributes practical validation that hybrid feature engineering remains competitive with neural approaches on small, low-resource corpora, a finding relevant to practitioners deploying sentiment systems in underserved languages.
MentionsIndonesian NLP · TF-IDF · Logistic Regression · MLP · Sentiment Analysis
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