| Title |
Topic Modeling of News Article about International Construction Market Using Latent Dirichlet Allocation |
| Authors |
문성현(Moon, Seonghyeon) ; 정세환(Chung, Sehwan) ; 지석호(Chi, Seokho) |
| DOI |
https://doi.org/10.12652/Ksce.2018.38.4.0595 |
| Keywords |
해외건설시장;뉴스기사;텍스트 마이닝;토픽 모델링;잠재 디리클레 할당 International construction market;News article;Text-mining;Topic modeling;Latent dirichlet allocation |
| Abstract |
Sufficient understanding of oversea construction market status is crucial to get profitability in the international construction project. Plenty of researchers have been considering the news article as a fine data source for figuring out the market condition, since the data includes market information such as political, economic, and social issue. Since the text data exists in unstructured format with huge size, various text-mining techniques were studied to reduce the unnecessary manpower, time, and cost to summarize the data. However, there are some limitations to extract the needed information from the news article because of the existence of various topics in the data. This research is aimed to overcome the problems and contribute to summarization of market status by performing topic modeling with Latent Dirichlet Allocation. With assuming that 10 topics existed in the corpus, the topics included projects for user convenience (topic-2), private supports to solve poverty problems in Africa (topic-4), and so on. By grouping the topics in the news articles, the results could improve extracting useful information and summarizing the market status. |