SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence
This work addresses business intelligence needs by developing a social listening system for Vietnamese e-commerce, though it is incremental as it applies known deep learning techniques to a new language-specific dataset.
The paper tackles aspect-based sentiment analysis in Vietnamese by creating a new dataset (UIT-ViSFD with 11,122 annotated comments) and proposes a Bi-LSTM model with fastText embeddings, achieving F1-scores of 84.48% for aspect extraction and 63.06% for sentiment classification, outperforming existing methods.
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.