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 blog messages, extracts terms, and forms document-term matrix over time. Then, the method adopts the latent semantic analysis and extract hidden topics (people's needs) over time. In our previous work, we already proposed the graph-based topic extraction method using the modularity measure. Our previous method could visualize topic structure transition, but could not extract clear topics. In this paper, to show the effectiveness of our proposed method, we provide the experimental results, and compare them with our previous method's results.