Abstract: A method, system and computer program product for identifying and quantifying sentiment and promotion bias. A set of sentiment as a measure of a group opinion is received, where a sentiment is expressed as a weight associated with an edge between two vertices in a graph. Balanced graphs associated with spanning trees corresponding to multiple views of a signed graph are constructed using the set of sentiments. Scores are assigned for each of the multiple views of the signed graph to determine an influence one group has over another group while maintaining agreement. An inequitable ratio of the assigned scores over agreeable sets of vertices of the signed graph is obtained. Based on the inequitable ratio of the assigned scores over agreeable sets of vertices of the signed graph, a more accurate sentiment bias is identified and quantified in contrast to currently existing sentiment analysis tools.

Abstract: A system and method for constructing a hierarchical multi-faceted classification structure includes organizing a plurality of visual categories into a multi-relational reference ontology that accounts for a plurality of different types of relationships. Media artifacts are categorized into the plurality of visual categories. The categories of artifacts are refined based on faceted ontology relationships or constraints from the multi-relational reference ontology. The multi-relational reference ontology and the one or more media artifacts with relationships are stored as the hierarchical multi-faceted classification structure in computer readable memory storage.

Abstract: A method and apparatus is provided to analyze social and content networks, to identify new relationships among users and content, and to more effectively link users to content. Embodiments of the invention also facilitate more relevant searches over both social and content data. One embodiment is directed to analyzing integrated social and content networks, and includes specifying a social network comprising multiple user nodes, and specifying a content network comprising multiple multimedia content nodes. Data elements associated with respective nodes of the user and content networks are analyzed, in order to detect a set of nodes comprising at least first and second user nodes and first and second content nodes, and to further detect at least three commonality links that join all the nodes of the set together. It is also determined whether a further link is a valid commonality link.

Abstract: A system and method for constructing a hierarchical multi-faceted classification structure includes organizing a plurality of visual categories into a multi-relational reference ontology that accounts for a plurality of different types of relationships. Media artifacts are categorized into the plurality of visual categories. The categories of artifacts are refined based on faceted ontology relationships or constraints from the multi-relational reference ontology. The multi-relational reference ontology and the one or more media artifacts with relationships are stored as the hierarchical multi-faceted classification structure in computer readable memory storage.

Abstract: The present invention is directed to a method and apparatus for assisting in rating and filtering multimedia content, such as images, videos and sound recordings. One embodiment comprises a computer implemented method for rating the objectionability of specified digital content that comprises one or more discrete content items, wherein the method includes the step of moving the specified content to one or more filtering stages in a succession of filtering stages. After the specified content is moved to a given one of the filtering stages, a rating procedure is carried out to determine whether a rating can be applied to one or more of the content items, and if so, a selected rating is applied to each of the one or more content items. The method further comprises moving content items of the specified content to the next stage in the succession after the given stage, when at least one content item of the specified content remains without rating, after the rating procedure at the given stage.

Abstract: A computer program product includes machine readable instructions for managing data items, the instructions stored on machine readable media, the product including instructions for: initializing a plurality of base models; minimizing a joint loss function to select models from the plurality for a plurality of labels associated with the data items; and at least one of sharing and combining the selected base models to formulate a composite classifier for each label. A computer system and additional computer program product are provided.

Abstract: A method and apparatus is disclosed that receives an input narrative in a particular media format, and produces therefrom a multimedia enriched version of the input narrative. In one embodiment, a method is provided for enriching an input narrative with multimedia content, wherein the method includes the step of selectively segmenting the input narrative to form queries pertaining to different portions or elements of the input narrative. The method further comprises retrieving multimedia artifacts from one or more multimedia repositories, wherein each retrieved multimedia artifact is associated with one of the queries, and selecting a subset of the retrieved multimedia artifacts, wherein the subset includes artifacts comprising different types of multimedia content. The input narrative is then combined with respective multimedia artifacts of the subset, in order to provide an enriched and enhanced narrative.

Abstract: A method and apparatus is provided for automatically classifying a multimedia artifact based on scoring, and selecting the appropriate set of ontologies from among all possible sets of ontologies, preferably using a recursive routing selection technique. The semantic tagging of the multimedia artifact is enhanced by applying only classifiers from the selected ontology, for use in classifying the multimedia artifact, wherein the classifiers are selected based on the context of the multimedia artifact. One embodiment of the invention, directed to a method for classifying a multimedia artifact, uses a specified criteria to select one or more ontologies, wherein the specified criteria indicates the comparative similarity between specified characteristics of the multimedia artifact and each ontology.

Abstract: The present invention is directed to a method and apparatus for assisting in rating and filtering multimedia content, such as images, videos and sound recordings. One embodiment comprises a computer implemented method for rating the objectionability of specified digital content that comprises one or more discrete content items, wherein the method includes the step of moving the specified content to one or more filtering stages in a succession of filtering stages. After the specified content is moved to a given one of the filtering stages, a rating procedure is carried out to determine whether a rating can be applied to one or more of the content items, and if so, a selected rating is applied to each of the one or more content items. The method further comprises moving content items of the specified content to the next stage in the succession after the given stage, when at least one content item of the specified content remains without rating, after the rating procedure at the given stage.

Abstract: A computer program product includes machine readable instructions for managing data items, the instructions stored on machine readable media, the product including instructions for: initializing a plurality of base models; minimizing a joint loss function to select models from the plurality for a plurality of labels associated with the data items; and at least one of sharing and combining the selected base models to formulate a composite classifier for each label. A computer system and additional computer program product are provided.

Abstract: A method and apparatus is disclosed that receives an input narrative in a particular media format, and produces therefrom a multimedia enriched version of the input narrative. In one embodiment, a method is provided for enriching an input narrative with multimedia content, wherein the method includes the step of selectively segmenting the input narrative to form queries pertaining to different portions or elements of the input narrative. The method further comprises retrieving multimedia artifacts from one or more multimedia repositories, wherein each retrieved multimedia artifact is associated with one of the queries, and selecting a subset of the retrieved multimedia artifacts, wherein the subset includes artifacts comprising different types of multimedia content. The input narrative is then combined with respective multimedia artifacts of the subset, in order to provide an enriched and enhanced narrative.

Abstract: A method and apparatus is provided to analyze social and content networks, to identify new relationships among users and content, and to more effectively link users to content. Embodiments of the invention also facilitate more relevant searches over both social and content data. One embodiment is directed to analyzing integrated social and content networks, and includes specifying a social network comprising multiple user nodes, and specifying a content network comprising multiple multimedia content nodes. Data elements associated with respective nodes of the user and content networks are analyzed, in order to detect a set of nodes comprising at least first and second user nodes and first and second content nodes, and to further detect at least three commonality links that join all the nodes of the set together. It is also determined whether a further link is a valid commonality link.

Abstract: Embodiments of the invention exploit the complementary and correlated information that is available across multiple channels of digital multimedia distribution, in order to ensure more efficient multimedia data access. One embodiment of the invention, directed to a method for generating a response to a specified request for information, is associated with multiple channels that are each adapted to carry and disseminate data content. The method comprises extracting data elements from each of the multiple channels, wherein each extracted data element pertains to one dimension of a plurality of correlation related dimensions. The method further comprises assigning each extracted data element to one of a plurality of correlation sets, wherein all the extracted data elements assigned to a particular set pertain to the same dimension, and each set is assigned data elements extracted from two or more different channels.

Abstract: A system and method for categorizing non-textual subject data, such as digital images, utilizes content-based data and meta-data to determine outcomes of classification tasks. The classification system has a modular architecture in which modules configured to perform specific functions, including algorithmic functions, can be integrated or deleted from the system. At the center of the classification system is a decision module comprising: (1) a task component having a number of classification tasks arranged within a task tree configuration, (2) an algorithmic component for selecting an algorithm for each classification task, (3) a sub-algorithmic component for selecting sub-algorithmic routines for each algorithm, and (4) a learning component for constructing and modifying the arrangement of the task tree and the classification tasks based on the frequencies of occurrences for the classes associated with a set of files.

Abstract: A system and method for categorizing non-textual subject data, such as digital images, utilizes content-based data and meta-data to determine outcomes of classification tasks. The classification system has a modular architecture in which modules configured to perform specific functions, including algorithmic functions, can be integrated or deleted from the system. At the center of the classification system is a decision module comprising: (1) a task component having a number of classification tasks arranged within a task tree configuration, (2) an algorithmic component for selecting an algorithm for each classification task, (3) a sub-algorithmic component for selecting sub-algorithmic routines for each algorithm, and (4) a learning component for constructing and modifying the arrangement of the task tree and the classification tasks based on the frequencies of occurrences for the classes associated with a set of files.