Product sales forecasting using macroeconomic indicators and online reviews: a method combining prospect theory and sentiment analysis

  • Chuan Zhang
  • Yu-Xin Tian
  • Zhi-Ping FanEmail author
  • Yang Liu
  • Ling-Wei Fan


Macroeconomic conditions and users’ word of mouth have significant impacts on the purchase decisions of consumers, and they can be potentially used to conduct better sales forecasts, but study on this aspect is relatively scarce. In this paper, a novel method for forecasting product sales based on macroeconomic indicators and online reviews is developed. Firstly, an algorithm is given to select proper macroeconomic indicators to capture the long-term trends of sales. Subsequently, an algorithm for sentiment analysis is given to convert textual online reviews into numerical digits, and the word-of-mouth effect is calculated by incorporating the data related to online reviews (e.g., ratings, browsing numbers, and approval numbers). The sentiment index of word-of-mouth effect is measured based on the prospect theory, which can accurately reflect the phenomenon whereby negative reviews seriously affect the purchasing decisions of consumers. Further, according to the selected macroeconomic indicators and the obtained sentiment index, a logarithmic autoregressive model for product sales forecasting is constructed, and the model parameters are estimated by the Adam optimizer. Finally, experimental studies on forecasting the sales volume of the Audi A6L in the next three quarters are conducted. The experimental results show that the performance of the proposed method is significantly better than the existing methods.


Sales forecasting Macroeconomic indicators Sentiment analysis Prospect theory Logarithmic autoregressive model 



This work was partially supported by the National Science Foundation of China (Project Nos. 71871049 and 71771043), the Fundamental Research Funds for the Central Universities, China (Project No. N170605001), and the 111 Project (B16009).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Chuan Zhang
    • 1
  • Yu-Xin Tian
    • 1
  • Zhi-Ping Fan
    • 1
    • 2
    Email author
  • Yang Liu
    • 1
  • Ling-Wei Fan
    • 1
  1. 1.School of Business AdministrationNortheastern UniversityShenyangChina
  2. 2.State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyangChina

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