SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence

Luong Luc Phan, Phuc Huynh Pham, Kim Thi-Thanh Nguyen, Sieu Khai Huynh, Tham Thi Nguyen, Luan Thanh Nguyen, Tin Van Huynh, Kiet Van Nguyen

Abstract: 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.