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http://dx.doi.org/10.7840/kics.2017.42.2.471

Investigations on Techniques and Applications of Text Analytics  

Kim, Namgyu (Kookmin University School of MIS)
Lee, Donghoon (Kookmin University The Graduate School of Business Information Technology)
Choi, Hochang (Kookmin University The Graduate School of Business Information Technology)
Wong, William Xiu Shun (Kookmin University The Graduate School of Business Information Technology)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.42, no.2, 2017 , pp. 471-492 More about this Journal
Abstract
The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.
Keywords
Big Data; Data Mining; Text Analytics; Topic Modeling;
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