IndoBERT-Sentiment: Context-Conditioned Sentiment Classification for Indonesian Text

2026-04-08

AI Summary

This study addresses the challenge of context-dependent sentiment misclassification in Indonesian text, a limitation commonly observed in existing models that disregard topical context. To overcome this, the authors propose a joint modeling approach that integrates topical context with textual content, marking the first successful application of context-conditioned modeling to Indonesian sentiment analysis. Built upon the IndoBERT Large architecture (335 million parameters), the model is trained on 31,360 context–text pairs spanning 188 distinct topics. Evaluated on a held-out test set, it achieves an accuracy of 88.1% and a macro F1-score of 0.856, representing a substantial improvement of 35.6 F1 points over the strongest baseline. This advancement demonstrates the critical role of contextual information in enhancing sentiment classification performance for Indonesian.

Technology Category

• Natural Language Processing: Text Classification & Sentiment Analysis
• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Existing Indonesian sentiment analysis models classify text in isolation, ignoring the topical context that often determines whether a statement is positive, negative, or neutral. We introduce IndoBERT-Sentiment, a context-conditioned sentiment classifier that takes both a topical context and a text as input, producing sentiment predictions grounded in the topic being discussed. Built on IndoBERT Large (335M parameters) and trained on 31,360 context-text pairs labeled across 188 topics, the model achieves an F1 macro of 0.856 and accuracy of 88.1%. In a head-to-head evaluation against three widely used general-purpose Indonesian sentiment models on the same test set, IndoBERT-Sentiment outperforms the best baseline by 35.6 F1 points. We show that context-conditioning, previously demonstrated for relevancy classification, transfers effectively to sentiment analysis and enables the model to correctly classify texts that are systematically misclassified by context-free approaches.

Problem

• sentiment analysis
• context-conditioned classification
• Indonesian text
• topical context
• context-free approaches

Innovation

• context-conditioned sentiment analysis
• IndoBERT
• topical context
• sentiment classification
• Indonesian NLP