  Research has shown that Convolutional Neural Networks (CNN) can be
effectively applied to text classification as part of a predictive coding
protocol. That said, most research to date has been conducted on data sets with
short documents that do not reflect the variety of documents in real world
document reviews. Using data from four actual reviews with documents of varying
lengths, we compared CNN with other popular machine learning algorithms for
text classification, including Logistic Regression, Support Vector Machine, and
Random Forest. For each data set, classification models were trained with
different training sample sizes using different learning algorithms. These
models were then evaluated using a large randomly sampled test set of
documents, and the results were compared using precision and recall curves. Our
study demonstrates that CNN performed well, but that there was no single
algorithm that performed the best across the combination of data sets and
training sample sizes. These results will help advance research into the legal
profession's use of machine learning algorithms that maximize performance.
