In many software development projects, people tend to repeat same mistakes due to lack of shared knowledge from past experiences. Generally, it is very difficult to manually find out valuable phenomena from huge data. Invisible context, which cannot be known directly from software documents or formal reports, is an important factor to these difficulties. We propose a new method to find contexts based on analysis to email archives in a project repository. In this method, we first apply natural language processing to extract keywords from email messages. Next, similarities among the messages are calculated based on the extracted keywords, and the messages are classified into clusters according to the similarities. The clustering result can be presented with other information such as code growth graph or schedule charts. This method is implemented as an extension to the Project Replayer, a tool to review past project data. Pilot analysis confirms that a researcher could grasp important contexts of failures in actual projects using the Project Replayer.