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 dataset built based on a strict annotation scheme 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 (in 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. Lastly, we build SA2SL, a social listening system based on the best performance model on our dataset, which will inspire more social listening systems in the future.
CITATION STYLE
Luc Phan, L., Huynh Pham, P., Thi-Thanh Nguyen, K., Khai Huynh, S., Thi Nguyen, T., Thanh Nguyen, L., … Van Nguyen, K. (2021). SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12816 LNAI, pp. 647–658). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-82147-0_53
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