Abstract
Recently, data analysis has been actively conducting. In particular, meaning in a sentence is determined by using the frequency of unstructured data such as SNS and opinions which are analyzed on a specific topic based on positive or negative words. However, this analysis involving only a simple word frequency or a specific word which includes unnecessary data. In this paper, we use the analytic technique of the referred documents, exponentialize the frequency of words. We integrate the frequency of words in exponentiated sentences with each other, and finally implement an analytical model that can quantitatively compare sentences.
The original version of this chapter was revised: For detailed information please see Erratum. The erratum to this chapter is available at https://doi.org/10.1007/978-981-10-7605-3_235
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Griffiths, T.-L., Steyvers, M., Tenenbaum, J.B.: Topics in semantic representation. Psychol. Rev. 114(2), 211–244 (2007)
Landauer, T.K., McNamara, D.S., Dennis, S., Kintsch, W.: Handbook of Latent Semantic Analysis. Lawrence Erlbaum Associates Inc., Mahwah (2007)
Evangelopoulos, N.E.: Latent semantic analysis. Wiley Interdisc. Rev. Cognit. Sci. 4(6), 683–692 (2013)
Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37, 159–164 (2010)
Huh, J.-H., Otgonchimeg, S., Seo, K.: Advanced metering infrastructure design and test bed experiment using intelligent agents: focusing on the PLC network base technology for Smart Grid system. J. Supercomput. 72(5), 1862–1877 (2016)
Kim, Y., Chung, M.: Unstructured data service model utilizing context-aware big data analysis. Adv. Comput. Sci. Ubiquit. Comput. 421, 926–931 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kim, Y., Chung, M. (2018). An Efficient Clustering Technique for Unstructured Data Utilizing Latent Semantic Analysis. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_38
Download citation
DOI: https://doi.org/10.1007/978-981-10-7605-3_38
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7604-6
Online ISBN: 978-981-10-7605-3
eBook Packages: EngineeringEngineering (R0)