Temporal awareness of changes in afflicted people's needs after East Japan Great Earthquake

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.