  Consumers often read product reviews to inform their buying decision, as some
consumers want to know a specific component of a product. However, because
typical sentences on product reviews contain various details, users must
identify sentences about components they want to know amongst the many reviews.
Therefore, we aimed to develop a system that identifies and collects component
and aspect information of products in sentences. Our BERT-based classifiers
assign labels referring to components and aspects to sentences in reviews and
extract sentences with comments on specific components and aspects. We
determined proper labels based for the words identified through pattern
matching from product reviews to create the training data. Because we could not
use the words as labels, we carefully created labels covering the meanings of
the words. However, the training data was imbalanced on component and aspect
pairs. We introduced a data augmentation method using WordNet to reduce the
bias. Our evaluation demonstrates that the system can determine labels for road
bikes using pattern matching, covering more than 88\% of the indicators of
components and aspects on e-commerce sites. Moreover, our data augmentation
method can improve the-F1-measure on insufficient data from 0.66 to 0.76.

Computation and Language