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Business Intelligence from Social Media: A Study from the VAST Box Office Challenge
ISSN: 0272-1716
Yafeng Lu, Arizona State University, Tempe
Feng Wang, Arizona State University, Tempe
Ross Maciejewski, Arizona State University, Tempe
With over 16 million Tweets per hour, 600 new blogs posts per minute and 400 million active users on Facebook, businesses have begun searching for ways to turn real-time consumer based posts into actionable intelligence. The goal is to extract information from this noisy, unstructured data and use it for trend analysis and prediction. Current practices support the notion visual analytics can play a large role in enabling the effective analysis of such data. However, empirical evidence demonstrating the effectiveness of a visual analytics solution is still lacking. This paper presents a visual analytics system which extracts data from Bitly and Twitter to use for box office revenue and user rating predictions. Results from the VAST Box Office Challenge 2013 demonstrate the benefit of an interactive environment for predictive analysis compared to a purely statistical modeling approach. These visual analysis method used in our system can be generalized to other domain where social media data is involved, such as sales forecasting, advertisement analysis, etc.
Yafeng Lu, Feng Wang, Ross Maciejewski, "Business Intelligence from Social Media: A Study from the VAST Box Office Challenge," IEEE Computer Graphics and Applications, 28 May 2014. IEEE computer Society Digital Library. IEEE Computer Society, <>
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