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arXiv · 2604.07057

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

Muhammad Apriandito Arya Saputra·Andry Alamsyah·Dian Puteri Ramadhani·Thomhert Suprapto Siadari·Hanif Fakhrurroja
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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.

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Muhammad Apriandito Arya Saputra, Andry Alamsyah, Dian Puteri Ramadhani, Thomhert Suprapto Siadari, Hanif Fakhrurroja. 2026-04-08. IndoBERT-Sentiment: Context-Conditioned Sentiment Classification for Indonesian Text. https://arxiv.org/abs/2604.07057

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