## 1 Introduction  
Natural language processing (NLP) enables computers to understand text and spoken words similar to humans [1]. Historically, rule- and statistics-based methods have been used to understand natural languages [2]. In the 2010s, representation learning and machine learning, including deep learning methods, became widespread in NLP, demonstrating high performance [3]. These methods achieved state-of-the-art results in many NLP tasks, such as text classification, named entity recognition, machine translation, sentiment analysis, text summarization, question answering, and text generation. This successful transition changed the research paradigm in the field, and researchers have been developing large and complex language models. Text classification involves categorizing text documents into predefined classes or categories. It can be used for tasks such as sentiment analysis, spam detection, and topic categorization. In particular, news topic categorization plays a crucial role in the world of journalism and information dissemination. News outlets produce large amounts of content daily. Categorization helps organize this information, making it easier for readers to find articles of interest quickly. Also, readers often prefer specific news topics or sections. Categorization allows news websites to provide tailored content recommendations, increasing user engagement and retention; effective topic categorization also enhances search functionality on news websites. Users can search for news articles by topic, improving their ability to access relevant information. By analyzing users' preferences and behavior, news websites can personalize the content they display, increasing user satisfaction. In summary, news topic categorization is a vital NLP task that enhances the accessibility, relevance, and user experience of news content. It also has broader implications for content personalization, trend analysis, and the efficient operation of news organizations. News articles can be generated by different countries in different languages; it is essential to investigate the characteristics of each language to understand the explicit and implicit contexts. However, most previous studies have focused on developing models to categorize news articles in English, and these models have been used for other languages. Without considering each language's characteristics and applying appropriate preprocessing methods, categorizing news topics is poorly performed. In particular, the Korean language (Hangul) is an agglutinative language that includes a set of morphemes with affixes. Agglutinative languages have the following characteristics. A formal morpheme conjunction is combined with the root of the real morpheme to derive each word or show a grammatical relationship. In Korean, the vocabulary is combined with prefixes and suffixes, resulting in complex and varying forms. Morphological analysis is the first step in the process of structuring natural language sentences. Therefore, morphological analysis is a necessary process for understanding Korean. Although several morphological analyzers have been used to extract essential morphemes to understand the Korean language, it is still unknown how each morphological analyzer extracts meaningful morphemes, considering both computational time and performance. To bridge such a research gap, the aim of this study was to investigate five different morphological analyzers and use them as a preprocessing step for topic categorization in news articles. Further, this study investigated the computational time of extracting morphemes by using the morphological analyzers from text and the accuracy of news topic categorization in a large corpus. To do this, term frequency-inverse document frequency [4] and light gradient boosting machine [5] were used for feature extraction and classification, respectively.