A BERT-based review helpfulness prediction model utilizing consistency of ratings and texts

Abstract

Predicting review helpfulness (RH) to ensure that consumers make effective purchasing decisions is a significant area of study. Many scholars have attempted to develop accurate review helpfulness prediction (RHP) methodologies. However, most previous studies have mainly focused on predictions using product review texts, and few studies have used product satisfaction as indicated by star ratings, particularly the consistency between review texts and star ratings. This study proposes a novel model called BHelP-CoRT (Bidirectional Encoder Representations from Transformers based RHP model utilizing consistency of ratings and texts) to predict RH. The proposed model consists of a review text encoder, star rating encoder, and text-rating interaction. The review text encoder was developed by applying the BERT model to extract contextual semantic features embedded in review texts. The star rating encoder was designed to embed star ratings into feature vectors. The text-rating interaction was constructed by applying an attention mechanism to extract the text-rating interaction and introduce consistency into the RHP tasks. This study conducted extensive experiments to demonstrate the effectiveness of the proposed model from multiple perspectives using real-world online reviews collected from Amazon. The experimental results show that the proposed model outperforms the state-of-the-art models, indicating that it can improve the RHP performance. Specifically, this effectiveness is reflected in the processing of reviews containing inconsistent information. This study supports the marketing efforts of the e-commerce industry by providing an RHP service to address consumer information overload.

Graphical abstract