Deep Learning Analysis for Reviews in Arabic E-Commerce Sites to Detect Consumer Behavior towards Sustainability

Abstract

Recently, online e-commerce has developed a major method for customers to buy various merchandise. Deep learning analysis of online customer reviews can detect consumer behavior towards sustainability. Artificial intelligence can obtain insights from product reviews to design sustainable products. A key challenge is that many sustainable products do not seem to fulfill consumers’ expectations due to the gap between consumers’ expectations and their knowledge of sustainable products. This article proposes a new deep learning model using dataset analysis and a neural computing dual attention model (DL-DA). The DL-DA model employs lexical analysis and deep learning methodology. The lexical analysis can detect lexical features in the customer reviews that emphasize sustainability. Then, the deep learning model extracts the main lexical and context features from the customer reviews. The deep learning model can predict customers’ repurchase habits concerning products that favor sustainability. This research collected data by crawling Arabic e-commerce websites for training and testing. The size of the collected dataset is about 323,150 customer reviews. The experimental results depict that the proposed model can efficiently enhance the accuracy of text lexical analysis. The proposed model achieves accuracy of 96.5% with an F1-score of 96.1%. We also compared the proposed model with state of the art models, where our model enhances both accuracy and sensitivity metrics by more than 5%.