Topic Extraction Analysis for Sidoardjo Mudflow Disaster Impacts

Yussanti Nur Fajrina, Yukari Shirota, Riri Fitri Sari

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present our work on analyzing the impact of the Mudflow Disaster in Sidoardjo, Indonesia, based on text mining technologies. We conducted a topic extraction using the Latent Dirichlet Allocation model. To handle the difficult expressions and grasp the points, we use various techniques such as bigram segmentation for documents related to the Mudflow in English. The TreeTagger is the morphological analysis tool used. The extracted topics clearly showed the impact of the Sidoardjo Mudflow. The most widely discussed topic found was the resettlement conditions and the compensation for the victim corresponding to the presidential regulation. We also found other frequently mentioned topics, such as the payment of resettlement, water pollution, and the verification process for the households.
Original languageEnglish
Pages (from-to)101-114
JournalGakushuin University Economics
Volume53
Issue number3
Publication statusPublished - 1 Oct 2016

Keywords

  • Topic extraction, Dirichlet Allocation Model, Sidoardjo Mudflow, Compensation, Resettlement, Presidential Regulation.

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