Abstract: Disclosed is a method and system for identifying an industry specific e-maven by analyzing textual data from social media. The system may collect textual data posted by users on social media. The system may then determine characteristics of the users by analyzing the textual data. The system may further calculate a maven score for the users by using the characteristics. The system may compute an industrial maven score for the users by using the maven scores and an industry adjustment factor. An industry specific e-maven may thus be identified based on the industry adjustment factor.

Abstract: A data extraction system for receiving and scanning documents to generate ordered input for storage in a database employs a non-linear statistical model for a data extraction sequence having a plurality of transformations. Each transformation transitions an extracted data value in various forms from a raw data image to a computed data value. For each transformation, a confidence model learns a confidence component for the particular transformation. The learned confidence components, generated from a control set of documents having known values, are employed in a production mode with actual raw data. The confidence component corresponds to a likelihood of transformation accuracy, and the confidence model aggregates the confidence components to compute a confidence for the extracted data value. A database stores the extracted data value labeled with the computed confidence attribute for subsequent use by an application employing the extracted data.