  There are large amounts of insight and social discovery potential in mining
crowd-sourced comments left on popular news forums like Reddit.com, Tumblr.com,
Facebook.com and Hacker News. Unfortunately, due the overwhelming amount of
participation with its varying quality of commentary, extracting value out of
such data isn't always obvious nor timely. By designing efficient, single-pass
and adaptive natural language filters to quickly prune spam, noise, copy-cats,
marketing diversions, and out-of-context posts, we can remove over a third of
entries and return the comments with a higher probability of relatedness to the
original article in question. The approach presented here uses an adaptive,
two-step filtering process. It first leverages the original article posted in
the thread as a starting corpus to parse comments by matching intersecting
words and term-ratio balance per sentence then grows the corpus by adding new
words harvested from high-matching comments to increase filtering accuracy over
time.
