Migrating applications to the cloud is rapidly increasing in many or- ganizations as it enables them to take advantages of the cloud, such as the lower costs and accessibility of data. Moreover, such organizations typically try to avoid sticking to a single cloud provider and rather prefer to be able to spread out their applications across different providers. However, there are many challenges in achieving this. First, many of the applications that are required to be moved to the cloud might be legacy applications that do not have good documentation, and so it is not trivial to even assess whether it is feasible to move them to the cloud or not. Moreover, such legacy applications might need a significant architecture overhaul to achieve the task of moving them to the cloud. Large client may have significant percentage of applications in this category. So, one has to evaluate cloud feasibility and understand whether there is a need to re-architect application based on what services providers are able to offer. Second, clients usually define multiple features, encryption/security level, and other service level requirements they expect in the providers they will migrate each of their applications to. Thus, choosing the right providers for different application is another challenging task here. In this work-in-progress paper, we present a novel methodology for prepar- ing such a cloud migration solution, where we perform text mining on application data to evaluate cloud-migration feasibility and then recommend the optimal so- lution using a mathematical optimization model. We illustrate our approach with an example use case.