  The rise in popularity of microblogging services like Twitter has led to
increased use of content annotation strategies like the hashtag. Hashtags
provide users with a tagging mechanism to help organize, group, and create
visibility for their posts. This is a simple idea but can be challenging for
the user in practice which leads to infrequent usage. In this paper, we will
investigate various methods of recommending hashtags as new posts are created
to encourage more widespread adoption and usage. Hashtag recommendation comes
with numerous challenges including processing huge volumes of streaming data
and content which is small and noisy. We will investigate preprocessing methods
to reduce noise in the data and determine an effective method of hashtag
recommendation based on the popular classification algorithms.
