## 5 Discussion  
In this study, five different morphological analyzers were investigated to categorize topics in news articles. Interestingly, the Mecab morphological analyzer achieved the best categorization accuracy compared to the others, including the baseline (Figure 4 and Table 3). The Mecab morphological analyzer is known for its robust tokenization  
Figure 4: Categorization accuracy across morphological analyzers. The boxplots indicate maximum, minimum, third quartile, first quartile, median (horizontal line in the box), and mean (symbol \(\times\)) values for each morphological analyzer. Baseline indicates categorization accuracy without applying a morphological analyzer. Figure 5: Bar plot of categorization performance for each morphological analyzer in seven categories. Note that each category has a different number datasets and this may affect the categorization performance. accuracy. It efficiently breaks down Korean text into meaningful tokens, which is essential for accurate categorization. Its dictionary-based approach and sophisticated algorithms enable it to handle various types of Korean text effectively. Also, the Mecab morphological analyzer provides detailed POS tagging, which can be valuable for text categorization. These characteristics may be a reason for achieving the best performance for news topic categorization. In contrast, the Hannanum morphological analyzer achieved the worst categorization accuracy among the five morphological analyzers. It has been criticized for its relatively weaker tokenization performance. It may struggle with certain complex or irregular word forms in Korean, leading to inaccuracies in tokenization. This can result in a less informative input representation for text categorization tasks, leading to lower accuracy. In addition, in terms of the computation time, Mecab and the Kkma achieved the fastest and slowest, respectively. Mecab employs a dictionary-based tokenization algorithm; this data structure allows for very efficient lookups and splitting of words based on dictionary entries. It prioritizes speed and simplicity, making it a popular choice for morphological analysis. In contrast, Kkma uses a more complex and computationally intensive approach based on recursiveness. This method involves recursively breaking down a sentence into its constituent morphemes, which can be a more time-consuming process. Agglutinative languages are characterized by their tendency to form words and convey grammatical information through the addition of discrete affixes [29]. In Korean, this phenomenon is observed in the structure of verbs and adjectives [30]. For instance, verb conjugations in Korean involve the attachment of suffixes to the verb stem to indicate tense, mood, aspect, and honorifics. This highly modular approach to morphological construction is a hallmark of agglutinative languages. The agglutinativeness of Korean is evident not only in its morphological structure but also in its syntax. The Korean language exhibits a consistent tendency to separate different grammatical elements within a sentence, with a clear demarcation between subject, object, verb, and modifiers. The use of postpositions to indicate grammatical relationships further underscores this agglutinative quality. In contrast to fusional languages, where multiple grammatical meanings may be compressed into a single morpheme, Korean employs a more transparent approach by adding discrete elements to convey distinct syntactic and semantic nuances. Therefore, morphological analysis for Korean plays a key role in enhancing the accuracy and effectiveness of topic categorization. This study explored how each morphological analyzer influences the performance of the topic categorization of news articles, and this study could provide new insight into the understanding of agglutinative languages. Topic categorization, a fundamental task in NLP, assumes a key role in the analysis and comprehension of news articles [31].