  E-Commerce (EC) websites provide a large amount of useful information that
exceed human cognitive processing ability. In order to help customers in
comparing alternatives when buying a product, previous studies designed opinion
summarization systems based on customer reviews. They ignored templates'
information provided by manufacturers, although these descriptive information
have much product aspects or characteristics. Therefore, this paper proposes a
methodology coined as SEOpinion (Summa-rization and Exploration of Opinions)
which provides a summary for the product aspects and spots opinion(s) regarding
them, using a combination of templates' information with the customer reviews
in two main phases. First, the Hierarchical Aspect Extraction (HAE) phase
creates a hierarchy of product aspects from the template. Subsequently, the
Hierarchical Aspect-based Opinion Summarization (HAOS) phase enriches this
hierarchy with customers' opinions; to be shown to other potential buyers. To
test the feasibility of using Deep Learning-based BERT techniques with our
approach, we have created a corpus by gathering information from the top five
EC websites for laptops. The experimental results show that Recurrent Neural
Network (RNN) achieves better results (77.4% and 82.6% in terms of F1-measure
for the first and second phase) than the Convolutional Neural Network (CNN) and
the Support Vector Machine (SVM) technique.
