  This research presents an analytical model that aims to pin-point influential
posts across a social web comprised of a corpus of posts. The model employs the
Latent Dirichlet Al-location algorithm to associate posts with topics, and the
TF-IDF metric to identify the key posts associated with each top-ic. The model
was demonstrated in the domain of customer relationship by enabling careful
monitoring of evolving "storms" created by individuals which tend to impact
large audiences (either positively or negatively). Future research should be
engaged in order to extend the scope of the corpus by including additional
relevant publicly available sources.
