Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis
Journal of Business ResearchPublished 2 February 2017
Zhi‐Ping Fan, Yu-Jie Che, Zhen-Yu Chen
Citations314
SJR quartileQ1
SJR score3.50
SNIP3.35
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TL;DR
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.
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
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