A New Korean Text Classification Benchmark for Recognizing the Political
  Intents in Online Newspapers
Beomjune Kim Eunsun Lee Dongbin Na
Many users reading online articles in various magazines may suffer
considerable difficulty in distinguishing the implicit intents in texts. In
this work, we focus on automatically recognizing the political intents of a
given online newspaper by understanding the context of the text. To solve this
task, we present a novel Korean text classification dataset that contains
various articles. We also provide deep-learning-based text classification
baseline models trained on the proposed dataset. Our dataset contains 12,000
news articles that may contain political intentions, from the politics section
of six of the most representative newspaper organizations in South Korea. All
the text samples are labeled simultaneously in two aspects (1) the level of
political orientation and (2) the level of pro-government. To the best of our
knowledge, our paper is the most large-scale Korean news dataset that contains
long text and addresses multi-task classification problems. We also train
recent state-of-the-art (SOTA) language models that are based on transformer
architectures and demonstrate that the trained models show decent text
classification performance. All the codes, datasets, and trained models are
available at https://github.com/Kdavid2355/KoPolitic-Benchmark-Dataset.