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

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 2; p. 636
• Main Authors: Mabrouk, Alhassan, Redondo, Rebeca P Díaz, Kayed, Mohammed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18-01-2021; MDPI
• Subjects: Attitude; BERT; Big Data; Commerce; Consumer Behavior; Consumers; Customers; deep learning techniques; Electronic commerce; hierarchical aspect-based opinion summarization; Humans; Neural networks; Neural Networks, Computer; Ontology; Product reviews; Recurrent neural networks; Sentiment analysis; Support Vector Machine; Support vector machines; web scraping; Websites; web scraping; deep learning techniques; hierarchical aspect-based opinion summarization; BERT; sentiment analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21020636

Recently, it has been found that e-commerce (EC) websites provide a large amount of useful information that exceed the human cognitive processing capacity. In order to help customers in comparing alternatives when buying a product, previous research authors have designed opinion summarization systems based on customer reviews. They ignored the template information provided by manufacturers, although its descriptive information has the most useful product characteristics and texts are linguistically correct, unlike reviews. Therefore, this paper proposes a methodology coined as SEOpinion (summarization and exploration of opinions) to summarize aspects and spot opinion(s) regarding them using a combination of template information with customer reviews in two main phases. First, the hierarchical aspect extraction (HAE) phase creates a hierarchy of 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 created a corpus by gathering information from the top five EC websites for laptops. The experimental results showed that recurrent neural network (RNN) achieved better results (77.4% and 82.6% in terms of F1-measure for the first and second phases, respectively) than the convolutional neural network (CNN) and the support vector machine (SVM) technique.