Hybrid model achieves strong Indonesian sentiment analysis results

Researchers have developed a hybrid approach for Indonesian sentiment analysis, combining TF-IDF text features with logistic regression and a neural network baseline. The study focused on classifying social media text into positive, negative, and neutral categories using a dataset of 707 samples. While the logistic regression model achieved competitive accuracy and F1 scores, the multilayer perceptron (MLP) neural network showed higher accuracy in experimental settings, though it was deemed less suitable for production deployment. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT This research demonstrates that traditional machine learning models can remain competitive for specific NLP tasks, even with smaller datasets.

RANK_REASON The cluster contains an academic paper detailing a new methodology for sentiment analysis. [lever_c_demoted from research: ic=1 ai=1.0]

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• Indonesian
• TF-IDF
• Logistic Regression
• MLP Neural Network
• Martin Clinton Tosima Manullang

• paper
• other