Abstract: Embodiments of the present invention provide a system, method, and article of hybrid human machine learning system with tagging and scoring techniques for sentiment magnitude scoring of textual passages. The combination of machine learning systems with data from human pooled language extraction techniques enable the present system to achieve high accuracy of human sentiment measurement and textual categorization of raw text, blog posts, and social media streams. This information can then be aggregated to provide brand and product strength analysis. A data processing module is configured to get streaming data and then tag the streaming data automatically using the machine learning output. A crowdsourcing module is configured to select a subset of social media posts that have been previously stored in the database, and present the social media posts on the web, which then tags each social media with a selected set of attributes.

Abstract: The present invention is directed to a method, system, and article of manufacture for systematically and automatically identifying abnormal or collective behavior patterns in microblogging messages that produce burst phenomena, such as Twitter storms. A microblogging storm engine in a storm detection server is configured to detect and classify the volume, shape, and type of a Twitter storm when keying on topics such as, but not limited to, a brand, an event, a person, an entity, a country, or a controversial issue. The microblogging storm engine comprises a storm detection module, a storm classification module, a database interface module, and a sentiment process module. The storm detection module is configured to detect different patterns of microblogging storms by capturing the volume of a particular storm to assist in output statistical analysis. The storm classification module is configured to classify the storms into different types of a particular storm category.