A New Korean Text Classification Benchmark for Recognizing the Political Intents in Online Newspapers
This work provides a new benchmark for political intent recognition in Korean text, addressing a domain-specific challenge for users and researchers, but it is incremental as it applies existing methods to new data.
The authors tackled the problem of automatically recognizing political intents in Korean online newspaper articles by creating a new dataset of 12,000 articles labeled for political orientation and pro-government level, and they demonstrated that trained transformer-based models achieve decent classification performance.
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.