  The exploration of social conversations for addressing patient's needs is an
important analytical task in which many scholarly publications are contributing
to fill the knowledge gap in this area. The main difficulty remains the
inability to turn such contributions into pragmatic processes the
pharmaceutical industry can leverage in order to generate insight from social
media data, which can be considered as one of the most challenging source of
information available today due to its sheer volume and noise. This study is
based on the work by Scott Spangler and Jeffrey Kreulen and applies it to
identify structure in social media through the extraction of a topical taxonomy
able to capture the latent knowledge in social conversations in health-related
sites. The mechanism for automatically identifying and generating a taxonomy
from social conversations is developed and pressured tested using public data
from media sites focused on the needs of cancer patients and their families.
Moreover, a novel method for generating the category's label and the
determination of an optimal number of categories is presented which extends
Scott and Jeffrey's research in a meaningful way. We assume the reader is
familiar with taxonomies, what they are and how they are used.
