Abstract: Embodiments of the present disclosure are directed to methods, computer program products, computer systems for providing a computing search platform for conducting opinion searches over the Internet concerning aggregated social media electronic messages about public opinions and public sentiments for a wide variety of matrices, such as social media posting of a particular industry over a specified time period, electronic social media posting on the public sentiments, public buzz, and public mood. Methods and systems of the present disclosure are directed to collecting and analyzing unstructured social media messages and correlating with structured entity representations in order to discern amount of interest in (buzz) and feelings about (mood) the real world organizations, people, products, and locations described by those entity representations transforming the data into a readily understandable visual display of the aggregated results on a computer display.

Abstract: Embodiments of the present disclosure are directed to methods, computer program products, computer systems for providing a computing search platform for conducting opinion searches over the Internet concerning aggregated social media electronic messages about public opinions and public sentiments for a wide variety of matrices, such as social media posting of a particular industry over a specified time period, electronic social media posting on the public sentiments, public buzz, and public mood. Methods and systems of the present disclosure are directed to collecting and analyzing unstructured social media messages and correlating with structured entity representations in order to discern amount of interest in (buzz) and feelings about (mood) the real world organizations, people, products, and locations described by those entity representations transforming the data into a readily understandable visual display of the aggregated results on a computer display.

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: 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.

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.