Preprint Article Version 2 This version is not peer-reviewed

Sales Prediction by Integrating Heat and Sentiments of Product Dimensions

Version 1 : Received: 26 December 2018 / Approved: 28 December 2018 / Online: 28 December 2018 (07:34:09 CET)
Version 2 : Received: 28 December 2018 / Approved: 29 December 2018 / Online: 29 December 2018 (05:28:21 CET)

How to cite: Lyu, X.; Jiang, C.; Ding, Y.; Wang, Z.; Liu, Y. Sales Prediction by Integrating Heat and Sentiments of Product Dimensions. Preprints 2018, 2018120340 (doi: 10.20944/preprints201812.0340.v2). Lyu, X.; Jiang, C.; Ding, Y.; Wang, Z.; Liu, Y. Sales Prediction by Integrating Heat and Sentiments of Product Dimensions. Preprints 2018, 2018120340 (doi: 10.20944/preprints201812.0340.v2).

Abstract

The accuracy of sales prediction models based on the big data of online word-of-mouth (eWOM) is still not satisfied. We argue that eWOM contains heat and sentiments of different product dimensions, which can improve the accuracy of these models. In this paper, we propose a dynamic topic analysis (DTA) framework in order to extract heat and sentiments of product dimensions from the big data of eWOM. Finally, we propose an autoregressive-heat-sentiment (ARHS) model, which integrates heat and sentiments of dimensions into the baseline predictive model. The empirical study in movie industry confirms that heat and sentiments of dimensions can improve the accuracy of sales prediction model. ARHS model is better for movie box-office revenue prediction than other models.

Subject Areas

big data; sales prediction; online word-of-mouth; dynamic topic model; dimension heat; dimension sentiment

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