  E-commerce dominates a large part of the world's economy with many websites
dedicated to online selling products. The vast majority of e-commerce websites
provide their customers with the ability to express their opinions about the
products/services they purchase. These feedback in the form of reviews
represent a rich source of information about the users' experiences and level
of satisfaction, which is of great benefit to both the producer and the
consumer. However, not all of these reviews are helpful/useful. The traditional
way of determining the helpfulness of a review is through the feedback from
human users. However, such a method does not necessarily cover all reviews.
Moreover, it has many issues like bias, high cost, etc. Thus, there is a need
to automate this process. This paper presents a set of machine learning (ML)
models to predict the helpfulness online reviews. Mainly, three approaches are
used: a supervised learning approach (using ML as well as deep learning (DL)
models), a semi-supervised approach (that combines DL models with word
embeddings), and pre-trained word embedding models that uses transfer learning
(TL). The latter two approaches are among the unique aspects of this paper as
they follow the recent trend of utilizing unlabeled text. The results show that
the proposed DL approaches have superiority over the traditional existing ones.
Moreover, the semi-supervised has a remarkable performance compared with the
other ones.
