Comparative Analysis of Deep Learning Models for Predicting Online Review Helpfulness

Abstract

The exponential growth of online customer reviews has created challenges for potential buyers to filter and identify helpful reviews, directly affecting their shopping experience. Accurate prediction of review helpfulness can improve the selection and presentation of valuable reviews, leading to a better user experience and more informed purchasing decisions. To address the limitations of traditional machine learning methods that rely on handcrafted features and fail to capture semantic context, this paper presents a comparative analysis of existing deep learning models to predict the helpfulness of online reviews. Our study employs larger and more diverse datasets from three popular e-commerce platforms: TripAdvisor, Amazon, and Yelp, and compares multiple deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and DistilBert, to identify the most accurate and effective predictions. Additionally, the study compares the deep learning models to the traditional machine learning algorithm XGBoost. Understanding the benefits and limitations of each model can lead to improved model selection and optimization, resulting in more accurate and efficient predictions for a wide range of applications. The results show that CNN consistently outperforms the other deep learning models and XGBoost regarding Mean Squared Error (MSE) and training time across all datasets.