Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product Reviews

Abstract: Businesses and customers can gain valuable information from product reviews. The sheer number of reviews often necessitates ranking them based on their potential helpfulness. However, only a few reviews ever receive any helpfulness votes on online marketplaces. Sorting all reviews based on the few existing votes can cause helpful reviews to go unnoticed because of the limited attention span of readers. The problem of review helpfulness prediction is even more important for higher review volumes, and newly written reviews or launched products. In this work we compare the use of RoBERTa and XLM-R LLMs to predict the helpfulness of online product reviews. The contributions of our work in relation to literature include extensively investigating the efficacy of state-of-the-art LLMs -- both monolingual and multilingual -- against a robust baseline, taking ranking metrics into account when assessing these approaches, and assessing multilingual models for the first time. We employ the Amazon review dataset for our experiments. According to our study on several product categories, multilingual and monolingual pre-trained LLMs outperform the baseline that utilizes random forest with handcrafted features as much as 23% in RMSE. Pre-trained LLMs reduce the need for complex text feature engineering. However, our results suggest that pre-trained multilingual models may not be used for fine-tuning only one language. We assess the performance of LLMs with and without additional features. Our results show that including additional features like product rating by the reviewer can further help the predictive methods.