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A0666
Title: Parallel sentiment analysis for sales forecasting with big data Authors:  Raymond Lau - City University of Hong Kong (Hong Kong) [presenting]
Abstract: While much research work has been devoted to 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 contribution is 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 for enhancing sales forecasting. 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. To our best knowledge, this is the first successful research of developing a parallel aspect-oriented sentiment analysis method for big data, and the application of such a method to enhance sales forecasting. 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.