Affected people's needs detection after the East Japan Great Earthquake — Time series analysis using LDA

Abstract

After the East Japan Great Earthquake happened on Mar. 11, 2011, many affected people who lost houses, jobs and families fell into difficulties. Governmental agencies and NPOs supported them by offering relief supplies, foods, evacuation centers and temporary houses. When various supports were offered to affected people, if Governmental agencies and NPOs could detect their needs appropriately, it was effective for supporting them. This paper proposes the method to extract affected people's needs from Social Media after the Earthquake and analyze their needs changes over time. We target the blog that expressed thoughts, requirements and complaints of affected people, and adopt the Latent Dirichlet Allocation (LDA) that is one of popular techniques for topic extraction. We then compare the analysis result with affected people's actual situation and real events and evaluate the effectiveness of our method. In addition, we evaluate the effectiveness as the method that can help decision making for providing appropriate supports to affected people.