Analyzing terrorist attacks is important for homeland security. Analyses of past records can provide important information on those attacks and enable appropriate actions to prevent similar attacks in the future. In this research, we present a novel method based on Latent Dirichlet Allocation to analyze data collected by START (Study of Terrorism and Responses to Terrorism) from 1970 to 2010. The first step in our method consists of generating topic models from the data. We then identify the most frequent terms occurring across various topic distributions. Moreover, we study the evolution of different kinds of attacks that occurred over time. The results show that a distinct change in attack patterns emerges over the past four decades.