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

TL;DR

This paper introduces a large-scale Korean news dataset for classifying political intent in online articles, along with baseline models demonstrating effective performance on multi-task classification tasks.

Contribution

It presents the first large-scale Korean news dataset with multi-task labels for political orientation and pro-government stance, and provides baseline deep learning models.

Findings

Deep learning models achieve decent classification accuracy.

The dataset is the largest of its kind for Korean political news.

Models trained on the dataset outperform previous benchmarks.

Abstract

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.