 Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysisZhi-Ping Fan, Yu-Jie Che and Zhen-Yu Chen Journal of Business Research, 2017, vol. 74, issue C, 90-100 Abstract: 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. Keywords: Product sales forecasting; Online reviews; Sentiment analysis; Bass model; Norton model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:74:y:2017:i:c:p:90-100 DOI: 10.1016/j.jbusres.2017.01.010 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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