This paper proposes a time series topic extraction method to investigate the transitions of people's needs after the East Japan Great Earthquake using latent semantic analysis. Our target data is a blog about afflicted people's needs provided by a non-profit organization in Tohoku, Japan. The method crawls blogmessages, extracts terms, and forms document-term matrix over time. Then, the method adopts the latent semantic analysis to extract people's needs as hidden topics. We recognize time series hidden topic-term matrix as 3rd order tensor, so that needs are detected transitions 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.
