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Many users reading online articles in various magazines may sufferconsiderable difficulty in distinguishing the implicit intents in texts. Inthis work, we focus on automatically recognizing the political intents of agiven online newspaper by understanding the context of the text. To solve thistask, we present a novel Korean text classification dataset that containsvarious articles. We also provide deep-learning-based text classificationbaseline models trained on the proposed dataset. Our dataset contains 12,000news articles that may contain political intentions, from the politics sectionof six of the most representative newspaper organizations in South Korea. Allthe text samples are labeled simultaneously in two aspects (1) the level ofpolitical orientation and (2) the level of pro-government. To the best of ourknowledge, our paper is the most large-scale Korean news dataset that containslong text and addresses multi-task classification problems. We also trainrecent state-of-the-art (SOTA) language models that are based on transformerarchitectures and demonstrate that the trained models show decent textclassification performance. All the codes, datasets, and trained models areavailable at https://github.com/Kdavid2355/KoPolitic-Benchmark-Dataset.

A New Korean Text Classification Benchmark for Recognizing the Political Intents in Online Newspapers