SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence (2105.15079v2)

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.

• The paper introduces SA2SL, a social listening system for Vietnamese mobile e-commerce that leverages Bi-LSTM with fastText embeddings for aspect-based sentiment analysis.
• It demonstrates superior F1-scores of 84.48% for aspect detection and 63.06% for sentiment classification compared to traditional models.
• The study provides a publicly available UIT-ViSFD dataset and suggests future directions using transformer models and reinforcement learning for enhanced sentiment detection.

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

The paper "SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence" presents a comprehensive approach to developing a sophisticated social listening system designed to support business intelligence. This research focuses on the Vietnamese context and utilizes aspect-based sentiment analysis (ABSA) as its backbone, expanding its utility from basic sentiment detection to a functional social listening application for mobile e-commerce. The following sections summarize the key contributions and findings of the paper.

Key Contributions

1. Creation of UIT-ViSFD Dataset: The authors have developed the UIT-ViSFD, a benchmark dataset for aspect-based sentiment analysis on Vietnamese smartphone feedback. This dataset comprises 11,122 human-annotated comments, and it addresses both aspect detection and sentiment classification tasks. It is made publicly available for research, providing a vital resource for further advancements in processing Vietnamese text.
2. Proposed Approach Using Bi-LSTM: The research introduces a novel method leveraging the Bi-LSTM architecture with fastText word embeddings to tackle the ABSA tasks. This approach is tailored for Vietnamese sentiment analysis and outperforms conventional machine learning models like Naive Bayes, SVM, and Random Forest, demonstrating superior F1-scores of 84.48% for aspect detection and 63.06% for sentiment classification.
3. Development of SA2SL System: Based on the successful application of the Bi-LSTM model, the authors have built SA2SL, a social listening system incorporating ABSA for smart analytical processes. This system is adept at categorizing user feedback and extracting nuanced sentiments regarding smartphone aspects, which aids in enriching the decision-making process both for customers and businesses.

Experimental Insights

The experiments reveal that deep learning models significantly enhance performance in ABSA tasks compared to traditional machine learning approaches. The Bi-LSTM-based model specifically proves its efficacy by achieving the highest scores on the set benchmarks. The precision, recall, and F1-score metrics clearly indicate the model’s robustness, especially in handling aspect detection. However, sentiment detection remains challenging, with a noted disparity between aspect and sentiment classification performances. Such findings prompt potential opportunities for further optimization and development of sentiment detection methods in Vietnamese NLP applications.

Theoretical and Practical Implications

This paper provides significant contributions to both theoretical frameworks and practical implementations of sentiment analysis systems. The adoption of ABSA in a low-resource language setting demonstrates how nuanced semantic understanding can be improved through tailored datasets and sophisticated models like Bi-LSTM. The introduction of SA2SL points to the practical application of these theoretical advancements, suggesting a pathway toward integrating sentiment analysis in business intelligence systems effectively.

Speculations on Future Developments

Future work could explore several promising directions based on this paper. Enhancing sentiment polarity detection through the use of transformer models like BERT or its Vietnamese adaptations could further improve accuracy. Additionally, integrating reinforcement learning for dynamic adjustment of sentiment classification parameters might yield more nuanced results. Expanding the UIT-ViSFD to cover more varied domains beyond smartphones could also enrich the dataset's utility, enabling cross-domain sentiment analysis applications.

In conclusion, this paper marks a significant step forward in aspect-based sentiment analysis for Vietnamese text, providing a robust foundation for future applications in social listening and business intelligence systems. By making the dataset publicly available and demonstrating the effectiveness of advanced model architectures, it lays the groundwork for substantial progress in the field.