Abstract
Sellers readily obtain consumer product evaluations from online reviews in order to identify competitive products in detail and predict sales. Firstly, we collect product review data from shopping websites, social media, product communities, and other online platforms to identify product competitors with the help of word-frequency cooccurrence technology. We take mobile phones as an example to mine and analyze product competition information. Then, we calculate the product review quantity, review emotion value, product-network heat, and price statistics and establish the regression model of online product review forecasts. In addition, the neural-network model is established to suggest that the relationships among factors are linear. On the basis of analyzing and discussing the impact of product sales of the competitors, product price, the emotional value of the reviews, and product-network popularity, we construct the sales forecast model. Finally, to verify the validity of the factor analysis affecting the sales and the rationality of the established model, actual sales data are used to further analyze and verify the model, showing that the model is reasonable and effective.
Highlights
Compared with traditional enterprises, internet companies have a unique advantage regarding the acquisition of consumer feedback, owing to online product transactions and feedback venues [9]
When studying consumer reviews for this research, it was found that consumers describe product features and apply emotional feedback in their comments, they compare their products with those of similar manufacturers, implying that are product sales influenced by the products themselves, they are influenced by competitors [13]
E previous research focused on the factors affecting consumer reviews and the relationship between consumer reviews’ emotional scores and product sales by empirical research. e research mainly considered the impact of the product review on the product itself and rarely involved the impact of other competitive products
Summary
Pant and Sheng [19] found that competitor recognition prediction models based on online indicators were much better than those using offline data, such as standard industry classification codes and company market value. Pan et al [31] used online review information and Yelp review data to find that negative reviews had a greater impact on consumer purchasing decisions than did positive ones. Owing to consumer online-shopping characteristics, the emotional analysis of product reviews has a variety of features. E repeated purchasing index in the Bass diffusion model can [48], for example, be estimated using the emotional tendency of consumer reviews, and historic sales data can be used to fit the model to improve the accuracy of forecasting [49]. On the basis of the improved evidence theory, Tang and Dong [52] proposed multiple evidence dynamic weighted combination forecast method. erefore, from the work of these scholars, we find that the content of consumer reviews can help us make more accurate estimations of prediction model parameters, improving prediction accuracy [53]
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