  In this paper, we attempt to classify tweets into root categories of the
Amazon browse node hierarchy using a set of tweets with browse node ID labels,
a much larger set of tweets without labels, and a set of Amazon reviews.
Examining twitter data presents unique challenges in that the samples are short
(under 140 characters) and often contain misspellings or abbreviations that are
trivial for a human to decipher but difficult for a computer to parse. A
variety of query and document expansion techniques are implemented in an effort
to improve information retrieval to modest success.
