Abstract: A set of vectors may be obtained. The vectors may be multi-dimensional vectors that are associated with and describe tokens from a first set of tokens from a corpus of sources. The description may be based in part on the relationship of the token to at least a portion of the remainder of the corpus. A set of sentiment scores may be obtained. The sentiment scores in the set of sentiment scores may describe a sentiment associated with a corresponding token that is described by a vector from the set of vectors. The set of vectors and the set of sentiment scores may be input into a pattern-recognizer pathway in a first neural network. A probability value of a potential future event may then be generated by the first neural network. The probability value may be based on the set of vectors and the set of sentiment scores.

Abstract: Systems and methods for customizing digital content based on consumer context data are disclosed. In embodiments, a computer-implemented method, comprises: determining, by a computing device, a goodness of fit of one or more digital content segments for a digital product based on asset success metrics indicating a trend in consumer consumption of content; identifying, by the computing device, a select digital content segment from the one or more digital content segments based on the goodness of fit; and incorporating, by the computing device, the select digital content segment into the digital product.

Abstract: Aspects of the present invention provide devices that subtitle streaming video with audio and identify a speaker in a streaming video with audio according to words spoken by the speaker matched to a cognitive print. The cognitive print includes traits classified according a hierarchical long short term model (LSTM). The hierarchical LSTM includes layers of LSTMs and each layer corresponds to the classification of one trait. A processor annotates a subtitle of the words spoken by the speaker, which decorates the subtitle with a label representative of the identified speaker, and streams the decorated subtitle with the streaming video with audio.

Abstract: Aspects of the present invention provide devices that subtitle streaming video with audio and identify a speaker in a streaming video with audio according to words spoken by the speaker matched to a cognitive print. The cognitive print includes traits classified according a hierarchical long short term model (LSTM). The hierarchical LSTM includes layers of LSTMs and each layer corresponds to the classification of one trait. A processor annotates a subtitle of the words spoken by the speaker, which decorates the subtitle with a label representative of the identified speaker, and streams the decorated subtitle with the streaming video with audio.

Abstract: Systems and methods for customizing digital content based on consumer context data are disclosed. In embodiments, a computer-implemented method, comprises: determining, by a computing device, a goodness of fit of one or more digital content segments for a digital product based on asset success metrics indicating a trend in consumer consumption of content; identifying, by the computing device, a select digital content segment from the one or more digital content segments based on the goodness of fit; and incorporating, by the computing device, the select digital content segment into the digital product.

Abstract: A set of vectors may be obtained. The vectors may be multi-dimensional vectors that are associated with and describe tokens from a first set of tokens from a corpus of sources. The description may be based in part on the relationship of the token to at least a portion of the remainder of the corpus. A set of sentiment scores may be obtained. The sentiment scores in the set of sentiment scores may describe a sentiment associated with a corresponding token that is described by a vector from the set of vectors. The set of vectors and the set of sentiment scores may be input into a pattern-recognizer pathway in a first neural network. A probability value of a potential future event may then be generated by the first neural network. The probability value may be based on the set of vectors and the set of sentiment scores.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service by use of a lighthouse structure further including a private display for subscribed data for an accessibility-layered communication service as encoded with a private key to make inaccessible to the spectators but to be decoded on a user device with the private key.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: collecting various word data from cross-domain sources and subject websites; assessing relevancy of feature vectors from external domains, live content of subject websites, and secondary terms derived from the live contents; expanding a language model for a domain by relevance passing a logistic regression threshold.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: determining that one or more word of a feature vector more supports than negates a language model corresponding to the domain based on a sensitivity of respective word. Words having acceptable sensitivities are added to the language model, and the language model is enhanced by use of machine learning in order to accurately and comprehensively model the language specific for the domain.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: collecting various word data from cross-domain sources and subject websites; assessing relevancy of feature vectors from external domains, live content of subject websites, and secondary terms derived from the live contents; expanding a language model for a domain by relevance passing a logistic regression threshold.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: determining that one or more word of a feature vector more supports than negates a language model corresponding to the domain based on a sensitivity of respective word. Words having acceptable sensitivities are added to the language model, and the language model is enhanced by use of machine learning in order to accurately and comprehensively model the language specific for the domain.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service by use of a lighthouse structure further including a private display for subscribed data for an accessibility-layered communication service as encoded with a private key to make inaccessible to the spectators but to be decoded on a user device with the private key.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service by use of a lighthouse structure further including a private display for subscribed data for an accessibility-layered communication service as encoded with a private key to make inaccessible to the spectators but to be decoded on a user device with the private key.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service further including a private display for subscribed event details as encoded with a key to make inaccessible to the spectators but to be decoded on a user device with the key.