Journal
JOURNAL OF BUSINESS RESEARCH
Volume 74, Issue -, Pages 90-100Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jbusres.2017.01.010
Keywords
Product sales forecasting; Online reviews; Sentiment analysis; Bass model; Norton model
Categories
Funding
- National Natural Science Foundation of China [71471035, 71571039]
- 111 Project [B16009]
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Online reviews provide consumers with rich information that may reduce their uncertainty regarding purchases. As such, these reviews have a significant influence on product sales. In this paper, a novel method that combines the Bass/Norton model and sentiment analysis while using historical sales data and online review data is developed for product sales forecasting. A sentiment analysis method, the Naive Bayes algorithm, is used to extract the sentiment index from the content of each online review and integrate it into the imitation coefficient of the Bass/Norton model to improve the forecasting accuracy. We collected real-world automotive industry data and related online reviews. The computational results indicate that the combination of the Bass/Norton model and sentiment analysis has higher forecasting accuracy than the standard Bass/Norton model and some other sales forecasting models. (C) 2017 Elsevier Inc. All rights reserved.
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