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

arXiv: https://arxiv.org/abs/2105.15079 · HTML: https://mlsanity.com/paper/2105.15079/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 9 of 9)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling](https://mlsanity.com/paper/1611.06639/) (2016)
- [Emotion Recognition for Vietnamese Social Media Text](https://mlsanity.com/paper/1911.09339/) (2019)
- [A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1609.02745/) (2016)
- [Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese](https://mlsanity.com/paper/2103.10069/) (2021)
- [A Simple and Efficient Ensemble Classifier Combining Multiple Neural Network Models on Social Media Datasets in Vietnamese](https://mlsanity.com/paper/2009.13060/) (2020)
- [Hate Speech Detection on Vietnamese Social Media Text using the Bi-GRU-LSTM-CNN Model](https://mlsanity.com/paper/1911.03644/) (2019)
- [Hate Speech Detection on Vietnamese Social Media Text using the Bidirectional-LSTM Model](https://mlsanity.com/paper/1911.03648/) (2019)

## Built on by (most cited first, 12 of 12)

- [VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding](https://mlsanity.com/paper/2403.15882/) (2024)
- [Detecting Spam Reviews on Vietnamese E-commerce Websites](https://mlsanity.com/paper/2207.14636/) (2022)
- [Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese Texts](https://mlsanity.com/paper/2210.02063/) (2022)
- [SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?](https://mlsanity.com/paper/2508.12243/) (2025)
- [Span Detection for Aspect-Based Sentiment Analysis in Vietnamese](https://mlsanity.com/paper/2110.07833/) (2021)
- [A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media](https://mlsanity.com/paper/2409.20467/) (2024)
- [Advancing Vietnamese Information Retrieval with Learning Objective and Benchmark](https://mlsanity.com/paper/2503.07470/) (2025)
- [Is word segmentation necessary for Vietnamese sentiment classification?](https://mlsanity.com/paper/2301.00418/) (2023)
- [New Benchmark Dataset and Fine-Grained Cross-Modal Fusion Framework for Vietnamese Multimodal Aspect-Category Sentiment Analysis](https://mlsanity.com/paper/2405.00543/) (2024)
- [ViGoEmotions: A Benchmark Dataset For Fine-grained Emotion Detection on Vietnamese Texts](https://mlsanity.com/paper/2602.08371/) (2026)
- [BamiBERT: A New BERT-based Language Model for Vietnamese](https://mlsanity.com/paper/2607.02259/) (2026)
- [ViTOED: A Dataset for Target-Oriented Emotion Detection on Vietnamese Social Media Texts](https://mlsanity.com/paper/2608.12776/) (2026)

## Abstract (arXiv)

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.