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Sustainability 2019, 11(3), 913; https://doi.org/10.3390/su11030913

Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions

1
School of Management, Hefei University of Technology, Hefei 230009, China
2
Key Laboratory of Process Optimization and Intelligent Decision Making of Ministry of Education, Hefei 230009, China
*
Authors to whom correspondence should be addressed.
Received: 20 January 2019 / Revised: 2 February 2019 / Accepted: 7 February 2019 / Published: 11 February 2019
(This article belongs to the Special Issue Big Data Research for Social Sciences and Social Impact)
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Abstract

Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used. View Full-Text
Keywords: big data; sales prediction; online word-of-mouth; dynamic topic model; product attributes; back-propagation neural network big data; sales prediction; online word-of-mouth; dynamic topic model; product attributes; back-propagation neural network
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Lyu, X.; Jiang, C.; Ding, Y.; Wang, Z.; Liu, Y. Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions. Sustainability 2019, 11, 913.

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