  Text classification is a significant branch of natural language processing,
and has many applications including document classification and sentiment
analysis. Unsurprisingly, those who do text classification are concerned with
the run-time of their algorithms, many of which depend on the size of the
corpus' vocabulary due to their bag-of-words representation. Although many
studies have examined the effect of preprocessing techniques on vocabulary size
and accuracy, none have examined how these methods affect a model's run-time.
To fill this gap, we provide a comprehensive study that examines how
preprocessing techniques affect the vocabulary size, model performance, and
model run-time, evaluating ten techniques over four models and two datasets. We
show that some individual methods can reduce run-time with no loss of accuracy,
while some combinations of methods can trade 2-5% of the accuracy for up to a
65% reduction of run-time. Furthermore, some combinations of preprocessing
techniques can even provide a 15% reduction in run-time while simultaneously
improving model accuracy.
