Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis |
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| Institution: | 1. Portsmouth Business School, University of Portsmouth, Portland Street, Portsmouth PO1 3DE, UK;2. Aston Business School, Aston University, Aston Triangle, Birmingham B4 7ET, UK;3. Cass Business School, City University London, 106 Bunhill Row, London EC1Y 8TZ, UK;1. School of Business, Chonbuk National University, Jeonju, Jeonbuk 561-756, Republic of Korea;2. School of Business, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada |
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| 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. |
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