Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN

Chuan Zhang, Yu-Xin Tian and Zhi-Ping Fan

International Journal of Forecasting, 2022, vol. 38, issue 3, 1005-1024

Abstract: Traditional sales forecasting methods are mainly based on historical sales data, which result in a certain lag. The relationship between sales volume and its influencing factors is intricate and often non-linear. In view of this, we propose a novel product forecasting method using online reviews and search engine data. Firstly, a dictionary-based sentiment analysis method is developed to convert the textual review concerning each attribute of the product into the corresponding sentiment score. And by combining the prospect theory and relevant online review data, sentiment indices in each period are calculated. Subsequently, data of product-related Baidu search words with different lag orders are collected and screened by time difference correlation analysis. Finally, the forecast model, PCA–DSFOA–BPNN, is constructed by combining the principal component analysis (PCA), the back propagation neural network (BPNN), and the improved fruit fly optimization algorithm (DSFOA), in which sentiment indices, Baidu search data, and historical sales volume are input data. Taking the monthly sales forecast of 14 automobile models as a case study, we observe that the proposed forecast method can effectively improve the forecast accuracy with good robustness.

Keywords: Sales forecasting; Online reviews; Search engine data; BPNN; Principal component analysis; Improved fruit fly optimization algorithm (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:3:p:1005-1024

DOI: 10.1016/j.ijforecast.2021.07.010

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