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 sentiment engine includes a sentiment module configured to gather opinions or determine sentiment expressed in documents, a crawling module configured to crawl servers to obtain at least a subset of the documents or opinions from social media websites, a keyword module configured to extract keywords from documents, a filtering module configured to filter keywords and documents, and a classification module configured to classify documents, sentences, and/or keywords, a polarity prediction module configured to predict the polarity of a sentiment sentence, and a social media net promoter score (SNPS) configured to calculate a loyalty metric of users from social media websites. The functionality of these modules may be combined with one another or in addition to other modules.

Abstract: A system, computer readable storage medium storing instructions, and computer-implemented method for determining sentiment expressed in documents is disclosed. A document is received from a plurality of documents. A sentence in the document that includes at least one sentiment signature within a predetermined distance of at least one keyword from a list of keywords is identified, wherein the list of keywords is extracted from the plurality of documents and is filtered using a phase transition formula, and wherein the at least one sentiment signature corresponds to an expression of at least one sentiment in the sentence. At least one category corresponding to the at least one keyword of the sentence is determined, wherein the at least one category is included in a list of categories that is generated using the list of keywords. At least one sentiment corresponding to the at least one category is determined based on the at least one sentiment signature.

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: The present invention is directed to a system, method, and article of manufacture that employs a sentiment engine for conducting sentiment and influence analysis of various types of messages from the social media hosts or websites to extract opinions on different categories, which includes services, products or hotels, and others, collectively referred to as “the keyword product”.

Abstract: The sentiment engine includes a sentiment module configured to gather opinions or determine sentiment expressed in documents, a crawling module configured to crawl servers to obtain at least a subset of the documents or opinions from social media websites, a keyword module configured to extract keywords from documents, a filtering module configured to filter keywords and documents, and a classification module configured to classify documents, sentences, and/or keywords, a polarity prediction module configured to predict the polarity of a sentiment sentence, and a social media net promoter score (SNPS) configured to calculate a loyalty metric of users from social media websites. The functionality of these modules may be combined with one another or in addition to other modules.

Abstract: A system, computer readable storage medium storing instructions, and computer-implemented method for determining sentiment expressed in documents is disclosed. A document is received from a plurality of documents. A sentence in the document that includes at least one sentiment signature within a predetermined distance of at least one keyword from a list of keywords is identified, wherein the list of keywords is extracted from the plurality of documents and is filtered using a phase transition formula, and wherein the at least one sentiment signature corresponds to an expression of at least one sentiment in the sentence. At least one category corresponding to the at least one keyword of the sentence is determined, wherein the at least one category is included in a list of categories that is generated using the list of keywords. At least one sentiment corresponding to the at least one category is determined based on the at least one sentiment signature.