  Social media datasets, especially Twitter tweets, are popular in the field of
text classification. Tweets are a valuable source of micro-text (sometimes
referred to as "micro-blogs"), and have been studied in domains such as
sentiment analysis, recommendation systems, spam detection, clustering, among
others. Tweets often include keywords referred to as "Hashtags" that can be
used as labels for the tweet. Using tweets encompassing 50 labels, we studied
the impact of word versus character-level feature selection and extraction on
different learners to solve a multi-class classification task. We show that
feature extraction of simple character-level groups performs better than simple
word groups and pre-processing methods like normalizing using Porter's Stemming
and Part-of-Speech ("POS")-Lemmatization.
