Online Sales Prediction: An Analysis with Dependency SCOR-Topic Sentiment Model

Abstract

This study aims to find a robust method to improve the accuracy of online sales prediction. Based on the groundings of existing literature, the authors proposed a Dependency SCOR-topic Sentiment (DSTS) model to analyze the online textual reviews and predict sales performance. The authors took the online sales data of tea as empirical evidence to test the proposed model by integrating the auto-regressive review information model into the DSTS model. The findings include: 1) the effect of the distribution of SCOR-topic from reviews on sales prediction; 2) the effect of review text sentiment on sales prediction increases as the specific topic probability dominates; and 3) the effect of review text sentiment on sales prediction increases as the rest topic probability evenly distributes. These findings demonstrate that the DSTS model is more precise than alternative methods in online sales prediction. This study not only contributes to the literature by pointing out how the distribution of sentiment topic impacts on sales prediction but also has practical implications for the e-commerce practitioners to manage the inventory better and advertise by this prediction method.