Multi-task learning (training one model for both sentiment and emotion at once) with BiLSTM outperforms single-task approaches on noisy, informal Indonesian text—and preprocessing with domain-specific slang dictionaries matters more than model complexity.
This paper tackles sentiment and emotion classification for Indonesian e-commerce reviews, which contain slang, regional words, and emoji that confuse standard tools. The authors built a two-track system: one using AutoML with TF-IDF features, and another using a BiLSTM neural network trained on both sentiment and emotion simultaneously.