SEOpinion: Summarization and Exploration Opinion of E-Commerce Websites

• URL: http://arxiv.org/abs/2312.14171v1
• Date: Tue, 12 Dec 2023 15:45:58 GMT
• Title: SEOpinion: Summarization and Exploration Opinion of E-Commerce Websites
• Authors: Alhassan Mabrouk and Rebeca P. D\'iaz-Redondo and Mohammed Kayed
• Abstract summary: This paper proposes a methodology coined as SEOpinion (Summa-rization and Exploration of Opinions) It 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. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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