Many network analysis tasks in social sciences rely on pre-existing data sources that were created with explicit relations or interactions between entities under consideration. Examples include email logs, friends and followers networks on social media, communication networks, etc. In these data, it is relatively easy to identify who is connected to whom and how they are connected. However, most of the data that we encounter on a daily basis are unstructured free-text data, e.g., forums, online marketplaces, etc. It is considerably more difficult to extract network data from unstructured text. In this work, we present an end-to-end system for analyzing unstructured text data and transforming the data into structured graphs that are directly applicable to a downstream application. Specifically, we look at social media data and attempt to predict the most indicative words from users' posts. The resulting keywords can be used to construct a context+content network for downstream processing such as graph-based analysis and learning. With that goal in mind, we apply our methods to the application of cross-domain entity resolution. The performance of the resulting system with automatic keywords shows improvement over the system with user-annotated hashtags.