  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.
