Text-Data Reduction Method to Grasp the Sequence of a Disaster Situation: Case Study of Web News Analysis of the 2015 Typhoons 17 and 18

Shosuke Sato; Toru Okamoto; Shunichi Koshimura

doi:10.20965/jdr.2017.p0329

Abstract

This study aims to compress web news, delivered as a big-data source after disasters. In this paper, article clustering, which is a combination of conventional means and an algorithm that selects the representative articles of each cluster, is designed and adopted. Experiments are conducted by evaluators. The proposed algorithm is in accord with the evaluators for 50% of the clustering and for about 30% to 40% of the representative-article selection.

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