SEOpinion: Summarization and Exploration Opinion of E-Commerce Websites
This addresses the problem of information overload for e-commerce customers by providing structured summaries, though it appears incremental as it builds on existing opinion summarization approaches.
The paper tackles the problem of summarizing product information from e-commerce websites by combining manufacturer templates with customer reviews, proposing a two-phase methodology called SEOpinion that extracts hierarchical aspects and summarizes opinions. Experimental results on a laptop dataset show that RNN achieves F1-measures of 77.4% and 82.6% in the two phases, outperforming CNN and SVM.
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.