IndoBERT-Sentiment：面向印度尼西亚文本的上下文条件情感分类
IndoBERT-Sentiment: Context-Conditioned Sentiment Classification for Indonesian Text
AI总结：
本文提出IndoBERT-Sentiment模型，通过结合上下文和文本进行情感分类，提升印度尼西亚文本的情感识别准确率。
摘要：
现有印度尼西亚情感分析模型在孤立分类时忽略上下文，而本文引入IndoBERT-Sentiment，通过结合上下文和文本进行情感分类，使预测基于讨论的主题。该模型基于IndoBERT Large（335M参数）训练于31,360个上下文-文本对，覆盖188个主题，达到F1宏平均0.856和准确率88.1%。在与三种通用印度尼西亚情感模型的对比测试中，IndoBERT-Sentiment在F1上领先基线35.6点。我们证明上下文条件化在相关性分类中已有效转移至情感分析，使模型能正确分类上下文无关方法系统性误判的文本。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
