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

Computational Intelligence - August 2016 - Cover1
Computational Intelligence - August 2016 - Cover2
Computational Intelligence - August 2016 - 1
Computational Intelligence - August 2016 - 2
Computational Intelligence - August 2016 - 3
Computational Intelligence - August 2016 - 4
Computational Intelligence - August 2016 - 5
Computational Intelligence - August 2016 - 6
Computational Intelligence - August 2016 - 7
Computational Intelligence - August 2016 - 8
Computational Intelligence - August 2016 - 9
Computational Intelligence - August 2016 - 10
Computational Intelligence - August 2016 - 11
Computational Intelligence - August 2016 - 12
Computational Intelligence - August 2016 - 13
Computational Intelligence - August 2016 - 14
Computational Intelligence - August 2016 - 15
Computational Intelligence - August 2016 - 16
Computational Intelligence - August 2016 - 17
Computational Intelligence - August 2016 - 18
Computational Intelligence - August 2016 - 19
Computational Intelligence - August 2016 - 20
Computational Intelligence - August 2016 - 21
Computational Intelligence - August 2016 - 22
Computational Intelligence - August 2016 - 23
Computational Intelligence - August 2016 - 24
Computational Intelligence - August 2016 - 25
Computational Intelligence - August 2016 - 26
Computational Intelligence - August 2016 - 27
Computational Intelligence - August 2016 - 28
Computational Intelligence - August 2016 - 29
Computational Intelligence - August 2016 - 30
Computational Intelligence - August 2016 - 31
Computational Intelligence - August 2016 - 32
Computational Intelligence - August 2016 - 33
Computational Intelligence - August 2016 - 34
Computational Intelligence - August 2016 - 35
Computational Intelligence - August 2016 - 36
Computational Intelligence - August 2016 - 37
Computational Intelligence - August 2016 - 38
Computational Intelligence - August 2016 - 39
Computational Intelligence - August 2016 - 40
Computational Intelligence - August 2016 - 41
Computational Intelligence - August 2016 - 42
Computational Intelligence - August 2016 - 43
Computational Intelligence - August 2016 - 44
Computational Intelligence - August 2016 - 45
Computational Intelligence - August 2016 - 46
Computational Intelligence - August 2016 - 47
Computational Intelligence - August 2016 - 48
Computational Intelligence - August 2016 - 49
Computational Intelligence - August 2016 - 50
Computational Intelligence - August 2016 - 51
Computational Intelligence - August 2016 - 52
Computational Intelligence - August 2016 - 53
Computational Intelligence - August 2016 - 54
Computational Intelligence - August 2016 - 55
Computational Intelligence - August 2016 - 56
Computational Intelligence - August 2016 - 57
Computational Intelligence - August 2016 - 58
Computational Intelligence - August 2016 - 59
Computational Intelligence - August 2016 - 60
Computational Intelligence - August 2016 - 61
Computational Intelligence - August 2016 - 62
Computational Intelligence - August 2016 - 63
Computational Intelligence - August 2016 - 64
Computational Intelligence - August 2016 - 65
Computational Intelligence - August 2016 - 66
Computational Intelligence - August 2016 - 67
Computational Intelligence - August 2016 - 68
Computational Intelligence - August 2016 - 69
Computational Intelligence - August 2016 - 70
Computational Intelligence - August 2016 - 71
Computational Intelligence - August 2016 - 72
Computational Intelligence - August 2016 - 73
Computational Intelligence - August 2016 - 74
Computational Intelligence - August 2016 - 75
Computational Intelligence - August 2016 - 76
Computational Intelligence - August 2016 - Cover3
Computational Intelligence - August 2016 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202311
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202308
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202305
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202302
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202211
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202208
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202205
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202202
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202111
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202108
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202105
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202102
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202011
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202008
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202005
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202002
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201911
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201908
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201905
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201902
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201811
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201808
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201805
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201802
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter12
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall12
https://www.nxtbookmedia.com