Unstructured Data Processing Using Keyword-Based Topic-Oriented Analysis

KIPS Transactions on Software and Data Engineering, Vol. 6, No.11, pp.521-526, November 2017
10.3745/KTSDE.2017.6.11.521, Full Text

Abstract

Data format of Big data is diverse and vast, and its generation speed is very fast, requiring new management and analysis methods, not traditional data processing methods. Textual mining techniques can be used to extract useful information from unstructured text written in human language in online documents on social networks. Identifying trends in the message of politics, economy, and culture left behind in social media is a factor in understanding what topics they are interested in. In this study, text mining was performed on online news related to a given keyword using topic - oriented analysis technique. We use Latent Dirichiet Allocation (LDA) to extract information from web documents and analyze which subjects are interested in a given keyword, and which topics are related to which core values are related.


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Cite this paper

[KIPS Transactions Style]
M. Ko, "Unstructured Data Processing Using Keyword-Based Topic-Oriented Analysis," KIPS Transactions on Software and Data Engineering, Vol.6, No.11, pp.521-526, 2017, DOI: 10.3745/KTSDE.2017.6.11.521.

[IEEE Style]
Myung-Sook Ko, "Unstructured Data Processing Using Keyword-Based Topic-Oriented Analysis," KIPS Transactions on Software and Data Engineering, vol. 6, no. 11, pp. 521-526, 2017. DOI: 10.3745/KTSDE.2017.6.11.521.

[ACM Style]
Ko, M. 2017. Unstructured Data Processing Using Keyword-Based Topic-Oriented Analysis. KIPS Transactions on Software and Data Engineering, 6, 11, (2017), 521-526. DOI: 10.3745/KTSDE.2017.6.11.521.