## 2 Related Works
### Morphological Analysis in News Topic Categorization  
Since morphological analysis plays a crucial role in extracting explicit and implicit contexts from sentences, previous studies attempted to use various morphological analyzers for topic categorization in news articles. In particular, the Korean language is known for its complex morphological structure. Words often change forms based on grammatical rules, making it challenging to identify the root or base form of a word. Morphological analysis helps in breaking down words into their constituent morphemes, providing a clear understanding of word structure.  
[PERSON] et al. [18] used a keyword extraction technique designed for tracking topics over time, particularly in the context of online news data. The authors proposed a two-stage keyword extraction system. They first extracted candidate keywords from documents and then removed meaningless words by comparing candidate keywords in terms of ranking results, which were based on the number of appearances. The results revealed that the keyword-extracting technique using cross-domain filtering can be applied to documents, but the study did not evaluate the effectiveness or performance. [PERSON] et al. [19] investigated topic category analysis in news articles, excluded special characters and numbers, and applied stemming for morpheme analysis. Finally, only nouns were used for topic categorization. For topic categorization, they used LDA and TF-IDF and found a categorization accuracy of approximately 80% in different configurations. They performed conventional morpheme analysis and did not evaluate its effectiveness. [PERSON] et al. [20] characterized news items at four levels by using computational techniques, including topic modeling and the vector representation of words and news items. They applied simple keyword extraction for morphological analysis and applied News2 Vec, a hierarchical clustering method, to construct a matrix to cluster news items. They found that differences in news-reading behavior across different demographic groups are the most noticeable at the subtopic level but are not prominent at the section or topic levels. [PERSON] et al. [21] investigated different oversampling methods, which are used to overcome the problem of imbalanced data, for topic categorization in news articles. For morphological analysis, they applied noun extraction. They found oversampling methods generally improve the performance of imbalanced and resampled data. [PERSON] et al. [22] investigated forecasting pork prices using topic modeling in news articles. They utilized news articles and retail price data and applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. In this keyword extraction, they applied tokenization and removed punctuation and stop-words for morphological analysis. They found that there was a strong relationship between the meaning of news articles and the price of pork. Morpheme analyses, strengths, and weaknesses in these studies are summarized in Table 1.  
Several previous studies attempted to apply morphological analysis and categorize topics in news articles, but the effectiveness of using morphological analyzers remained unknown, and categorization performance differed across the studies. Because news articles are generally written in various international languages, the suitability of morphological analyzers can influence the performance. Also, there are few studies on how such a morphological analyzers influence the categorization accuracy of news articles. For these reasons, this study investigated the effects of using morphological analyzers for topic categorization in terms of computational cost and performance.