Computational Intelligence - August 2016 - 35
and products for the purposes of document-level sentiment classification [11], [20]. However, existing studies have ignored the
temporal order of reviews that a user posted or a product
received. We argue that the temporal relations of reviews are
potentially helpful for learning user and product embeddings.
For example, a product that receives positive reviews initially
might be more likely to get positive reviews later on. Sequence
models, such as recurrent neural network (RNN), are effective
in learning temporal information, and have achieved excellent
performance on tasks with a focus on temporal sequences [21].
In this paper, therefore, we propose a sequence modelingbased neural network approach to embed temporal relations of
reviews into the categories of distributed user and product
representations (hereafter, user embeddings and product
embeddings for short) learning for the sentiment classification
of reviews, in which reviews written by one user or evaluated on
one product are considered as a temporal-ordered sequence.
Using a document-level composition model, which is a onedimensional convolutional neural network (1d-CNN), each
review is first represented by a review embedding.Then, all the
reviews by each user are sorted in a temporal order and every
review is labeled by its respective rating score. We call such a
IMAGES LICENSED BY INGRAM PUBLISHING
different users or written for different products are often skewed
in the real world [10]. Tang et al. reported that sentiment ratings
from the same user (or towards the same product) are more consistent than those from different users (or towards different products) [11]. As such, it motivated researchers to exploit user or
product information in sentiment analysis.
Some approaches extracted user, product and review features based on the Bag-of-Words assumption, which were then
subsequently incorporated into different machine learning classifiers [12], [13]. Others took advantage of topic models in
order to capture the interest distribution of users and the content distribution for products [14].
Recently, deep neural networks have been used for distributed representation learning [15], [16] and have shown promising
results in sentiment analysis [17], [18], [19]. It is possible to learn
the distributed representation of a user or a product, which
essentially captures semantic information contained in the
reviews posted by the user or via product evaluation. In such a
representation, a user or product is represented as a dense and
real-valued vector. In previous studies, user, product and review
information is incorporated into a purposely-built neural network model in order to learn distributed representations of users
auguSt 2016 | IEEE ComputatIonal IntEllIgEnCE magazInE
35
Table of Contents for the Digital Edition of Computational Intelligence - August 2016
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