  In this paper, we present a process of building a social listening system
based on aspect-based sentiment analysis in Vietnamese from creating a dataset
to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese
Smartphone Feedback Dataset as a new benchmark corpus built based on a strict
annotation schemes for evaluating aspect-based sentiment analysis, consisting
of 11,122 human-annotated comments for mobile e-commerce, which is freely
available for research purposes. We also present a proposed approach based on
the Bi-LSTM architecture with the fastText word embeddings for the Vietnamese
aspect based sentiment task. Our experiments show that our approach achieves
the best performances with the F1-score of 84.48% for the aspect task and
63.06% for the sentiment task, which performs several conventional machine
learning and deep learning systems. Last but not least, we build SA2SL, a
social listening system based on the best performance model on our dataset,
which will inspire more social listening systems in future.
