  In this paper we describe the implementation of a convolutional neural
network (CNN) used to assess online review helpfulness. To our knowledge, this
is the first use of this architecture to address this problem. We explore the
impact of two related factors impacting CNN performance: different word
embedding initializations and different input review lengths. We also propose
an approach to combining rating star information with review text to further
improve prediction accuracy. We demonstrate that this can improve the overall
accuracy by 2%. Finally, we evaluate the method on a benchmark dataset and show
an improvement in accuracy relative to published results for traditional
methods of 2.5% for a model trained using only review text and 4.24% for a
model trained on a combination of rating star information and review text.
