Towards Improving E-Commerce Customer Review Analysis for Arabic Language Opinion Mining

Abstract

In recent years, the rapid development of Internet-related technologies has facilitated the widespread adoption of online purchasing as a convenient means of satisfying consumer needs. Conducting sentiment analysis on user reviews on e-commerce platforms can substantially improve customer satisfaction. In order to resolve this issue, we propose a novel model for sentiment analysis that employs hybrid deep learning ensembles, combining RNN and TreeLSTM with AraBERT as the word embedding. Our research concentrates on creating a hybrid deep-learning model to predict Arabic sentiment accurately. We employ deep learning models with various word embeddings, such as RNN, LSTM, BiLSTM, TreeLSTM, RNN-LSTM, RNN-BiLSTM, and RNN-TreeLSTM. Multiple open-access datasets are used to evaluate the efficacy of the proposed model, including the BRAD dataset, the ARD dataset, and merged datasets containing 610,600 items. The experimental findings indicate that our proposed model is well-suited for evaluating the sentiments expressed in Arabic texts. Our strategy starts with extracting features using the Arabert model, followed by developing and training five hybrid deep-learning models. We attained a significant accuracy improvement of 0.9409 when comparing our method to traditional and hybrid deep learning techniques. This demonstrates that our proposed model precisely analyzes sentiment. These findings are important for enhancing the comprehension of emotions conveyed in Arabic text and have practical implications for various applications, especially e-commerce. By accurately assessing sentiment, businesses can better comprehend customer preferences and enhance consumer satisfaction by enhancing their offerings.