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http://dx.doi.org/10.3745/KTSDE.2021.10.6.223

A Study on Search Query Topics and Types using Topic Modeling and Principal Components Analysis  

Kang, Hyun-Ah (고려대학교 빅데이터융합학과)
Lim, Heui-Seok (고려대학교 컴퓨터학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.6, 2021 , pp. 223-234 More about this Journal
Abstract
Recent advances in the 4th Industrial Revolution have accelerated the change of the shopping behavior from offline to online. Search queries show customers' information needs most intensively in online shopping. However, there are not many search query research in the field of search, and most of the prior research in the field of search query research has been studied on a limited topic and data-based basis based on researchers' qualitative judgment. To this end, this study defines the type of search query with data-based quantitative methodology by applying machine learning to search research query field to define the 15 topics of search query by conducting topic modeling based on search query and clicked document information. Furthermore, we present a new classification system of new search query types representing searching behavior characteristics by extracting key variables through principal component analysis and analyzing. The results of this study are expected to contribute to the establishment of effective search services and the development of search systems.
Keywords
Search Query Types; Text Mining; Topic Modeling; PCA; Log Analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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