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

TL;DR

This paper develops a Vietnamese social listening system using aspect-based sentiment analysis, introducing a new dataset, a Bi-LSTM approach, and demonstrating effective performance for business intelligence applications.

Contribution

It introduces UIT-ViSFD, a new Vietnamese sentiment dataset, and presents a Bi-LSTM based method that outperforms existing models for aspect-based sentiment analysis.

Findings

Achieved F1-score of 84.48% for aspect detection

Achieved F1-score of 63.06% for sentiment classification

Built a practical social listening system for Vietnamese mobile feedback

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 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.