# SEOpinion: Summarization and Exploration Opinion of E-Commerce Websites

arXiv: https://arxiv.org/abs/2312.14171 · HTML: https://mlsanity.com/paper/2312.14171/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 72 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings](https://mlsanity.com/paper/1605.05397/) (2016)

## Built on by (most cited first, 2 of 2)

- [Pneumonia Detection on chest X-ray images Using Ensemble of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2312.07965/) (2023)
- [PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights](https://mlsanity.com/paper/2506.17314/) (2025)

## Abstract (arXiv)

E-Commerce (EC) websites provide a large amount of useful information that exceed human cognitive processing ability. In order to help customers in comparing alternatives when buying a product, previous studies designed opinion summarization systems based on customer reviews. They ignored templates' information provided by manufacturers, although these descriptive information have much product aspects or characteristics. Therefore, this paper proposes a methodology coined as SEOpinion (Summa-rization and Exploration of Opinions) which provides a summary for the product aspects and spots opinion(s) regarding them, using a combination of templates' information with the customer reviews in two main phases. First, the Hierarchical Aspect Extraction (HAE) phase creates a hierarchy of product aspects from the template. Subsequently, the Hierarchical Aspect-based Opinion Summarization (HAOS) phase enriches this hierarchy with customers' opinions; to be shown to other potential buyers. To test the feasibility of using Deep Learning-based BERT techniques with our approach, we have created a corpus by gathering information from the top five EC websites for laptops. The experimental results show that Recurrent Neural Network (RNN) achieves better results (77.4% and 82.6% in terms of F1-measure for the first and second phase) than the Convolutional Neural Network (CNN) and the Support Vector Machine (SVM) technique.