A Comparative Analysis of Cognitive Change about Big Data Using Social Media Data Analysis

KIPS Transactions on Software and Data Engineering, Vol. 6, No.7, pp.371-378, July 2017
10.3745/KTSDE.2017.6.7.371, Full Text

Abstract

Recently, with the spread of smart device and the introduction of web services, the data is rapidly increasing online, and it is utilized in various fields. In particular, the emergence of social media in the big data field has led to a rapid increase in the amount of unstructured data. In order to extract meaningful information from such unstructured data, interest in big data technology has increased in various fields. Big data is becoming a key resource in many areas. Big data's prospects for the future are positive, but concerns about data breaches and privacy are constantly being addressed. On this subject of big data, where positive and negative views coexist, the research of analyzing people’s opinions currently lack. In this study, we compared the changes in peoples perception on big data based on unstructured data collected from the social media using a text mining. As a results, yearly keywords for domestic big data, declining positive opinions, and increasing negative opinions were observed. Based on these results, we could predict the flow of domestic big data.


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Cite this paper

[KIPS Transactions Style]
Y. Yun, J. Jo, Y. Hur, and H. Lim, "A Comparative Analysis of Cognitive Change about Big Data Using Social Media Data Analysis," KIPS Transactions on Software and Data Engineering, Vol.6, No.7, pp.371-378, 2017, DOI: 10.3745/KTSDE.2017.6.7.371.

[IEEE Style]
Youdong Yun, Jaechoon Jo, Yuna Hur, and Heuiseok Lim, "A Comparative Analysis of Cognitive Change about Big Data Using Social Media Data Analysis," KIPS Transactions on Software and Data Engineering, vol. 6, no. 7, pp. 371-378, 2017. DOI: 10.3745/KTSDE.2017.6.7.371.

[ACM Style]
Yun, Y., Jo, J., Hur, Y., and Lim, H. 2017. A Comparative Analysis of Cognitive Change about Big Data Using Social Media Data Analysis. KIPS Transactions on Software and Data Engineering, 6, 7, (2017), 371-378. DOI: 10.3745/KTSDE.2017.6.7.371.