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http://dx.doi.org/10.12652/Ksce.2018.38.4.0595

Topic Modeling of News Article about International Construction Market Using Latent Dirichlet Allocation  

Moon, Seonghyeon (Seoul National University)
Chung, Sehwan (Seoul National University)
Chi, Seokho (Seoul National University, The Institute of Construction and Environmental Engineering (ICEE))
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.38, no.4, 2018 , pp. 595-599 More about this Journal
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.
Keywords
International construction market; News article; Text-mining; Topic modeling; Latent dirichlet allocation;
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