Mining Bilateral Reviews for Online Transaction Prediction: A Relational Topic Modeling Approach

On most e-commerce platforms, reviews are often written by buyers to evaluate sellers or products offered by sellers. In recent years, more and more platforms allowing both buyers and sellers to write reviews for each other have emerged. These bilateral reviews are important information sources in the decision-making process of both buyers and sellers but have not been properly investigated in the literature before. We develop a comprehensive relational topic modeling approach to analyze bilateral reviews to predict transaction results. The prediction results will enable the platform to increase the chance that the buyer and seller reach a transaction by presenting buyers with offerings that are more likely to lead to a transaction. Within the framework of the relational topic model, we embed a topic structure with both shared and corpus-specific topics to better handle text corpora generated from different sources. Our model facilitates the extraction of the appropriate topic structure from different document collections that helps enhance the transaction prediction performance. Comprehensive experiments conducted on real-world data sets collected from sharing economy platforms demonstrate that our new model significantly outperforms other alternatives. The robust results obtained from multiple sets of comparisons demonstrate the value of bilateral reviews if they are processed properly. Our approach can be applied to many platforms where bilateral reviews are available.