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Title: Online sales prediction : an analysis with dependency SCOR-topic sentiment model
Authors: Huang, LJ 
Dou, ZX
Hu, YJ
Huang, RY
Issue Date: 2019
Source: IEEE access, 2019, v. 7, p. 79791-79797
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.
Keywords: Sentiment analysis
SCOR-topic distribution
Sales prediction
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2919734
Rights: © 2019 IEEE. Translations and content mining are permitted for academic research only.
Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
The following publication L. Huang, Z. Dou, Y. Hu and R. Huang, "Online Sales Prediction: An Analysis With Dependency SCOR-Topic Sentiment Model," in IEEE Access, vol. 7, pp. 79791-79797, 2019 is available at
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