Parallel Aspect-Oriented Sentiment Analysis for Sales Forecasting with Big Data

While much research work has been devoted to supply chain management and demand forecast, research on designing big data analytics methodologies to enhance sales forecasting is seldom reported in existing literature. The big data of consumer-contributed product comments on online social media provide management with unprecedented opportunities to leverage collective consumer intelligence for enhancing supply chain management in general and sales forecasting in particular. The main contributions of our work presented in this study are as follows: (1) the design of a novel big data analytics methodology that is underpinned by a parallel aspect-oriented sentiment analysis algorithm for mining consumer intelligence from a huge number of online product comments; (2) the design and the large-scale empirical test of a sentiment enhanced sales forecasting method that is empowered by a parallel co-evolutionary extreme learning machine. Based on real-world big datasets, our experimental results confirm that consumer sentiments mined from big data can improve the accuracy of sales forecasting across predictive models and datasets. The managerial implication of our work is that firms can apply the proposed big data analytics methodology to enhance sales forecasting performance. Thereby, the problem of under/over-stocking is alleviated and customer satisfaction is improved.