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Author Hashimoto, T. ♦ Kuboyama, T. ♦ Chakraborty, B.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Media ♦ Earthquakes ♦ Time series analysis ♦ Vectors ♦ Matrix decomposition ♦ Semantics ♦ Blogs
Abstract This paper proposes a time series topic detection method to investigate changes in afflicted people's needs after the East Japan Great Earthquake using latent semantic analysis and singular vectors' pattern similarities. Our target data is a blog about afflicted people's needs provided by a non-profit organization in Tohoku, Japan. The method crawls blog messages, extracts terms, and forms document-term matrix over time. Then, it adopts the latent semantic analysis to extract people's needs as hidden topics from each snapshot matrix. We form time series hidden topic-term matrix as 3rd order tensor, so that changes in topics (people's needs) are detected by investigating time-series similarities between hidden topics. In this paper, to show the effectiveness of our proposed method, we also provide the experimental results.
Description Author affiliation: Comput. Centre, Gakushuin Univ., Tokyo, Japan (Kuboyama, T.) || Software & Inf. Sci., Iwate Prefectural Univ., Iwate, Japan (Chakraborty, B.) || Commerce & Econ., Chiba Univ. of Commerce, Chiba, Japan (Hashimoto, T.)
ISBN 9781479928255
ISSN 21593450
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-10-22
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479928279
Size (in Bytes) 1.38 MB
Page Count 6
Starting Page 1
Ending Page 6


Source: IEEE Xplore Digital Library