Predictive neural network with sentiment data

A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

• obtain a first set of entity vectors, wherein each entity vector in the first set of entity vectors is a multi-dimensional entity vector that is associated with and describes an entity from a first set of entities from a corpus of sources, wherein the description is based in part on a relationship of the entity to at least a portion of the remainder of the corpus of sources, and wherein the corpus of sources contains at least one natural-language source;
• obtain a first set of sentiment scores, wherein each sentiment score in the first set of sentiment scores describes a sentiment associated with a corresponding entity that is described by an entity vector in the first set of vectors;
• obtain a second set of vectors, wherein each vector in the second set of vectors is a multi-dimensional vector that describes a token from a first set of tokens from the corpus of sources, wherein the description is based in part on a relationship of the token to at least a portion of the remainder of the corpus;
• obtain a second set of sentiment scores, wherein each sentiment score in the second set of sentiment scores describes a sentiment associated with a corresponding token that is described by a vector in the second set of vectors;
• input the first set of entity vectors and the first set of sentiment scores into a first pattern-recognizer pathway in a first neural network;
• inputting the second set of vectors and the second set of second sentiment scores into a second pattern-recognizer pathway in a second neural network, wherein the first neural network and the second neural network are separate networks prior to generating the first probability value and the second probability value;
• generate, by the first neural network and based on the first set of entity vectors and the first set of sentiment scores, a first probability value of a first potential future event;
• and
• generate, by the second neural network and based on the second set of vectors and the second set of sentiment scores, a second probability value of a second potential future event.

A system comprising:

• a processor;
• and
• a memory in communication with the processor, the memory containing program instructions that, when executed by the processor, are configured to cause the processor to perform a method, the method comprising:
• analyzing, by a first neural network, a corpus of sources related to an identified subject matter, the corpus comprising at least one natural-language source;
• creating, based on the analyzing the corpus by the first neural network, an entity model in the first neural network;
• analyzing a second corpus of sources related to a potential future event, the second corpus comprising a second at least one natural-language source, the analyzing comprising:
• identifying, by the entity model in the first neural network, a list of entities related to the potential future event;
• identifying, by a concept mapper, a list of concepts related to the potential future event;
• and
• determining, by a sentiment classifier and for each entity in the list of entities, a sentiment score;
• inputting the list of entities, the list of concepts, and the second corpus into a second neural network;
• creating, by the second neural network and for an entity in the list of entities, an entity vector, wherein the entity vector is a multi-dimensional vector that defines the entity based in part on a relationship of the entity to at least a portion of a remainder of the second corpus;
• creating, by the second neural network and for a concept in the list of concepts, a concept vector, wherein the concept vector is a multi-dimensional vector that defines the concept based in part on a relationship of the concept to at least a portion of the remainder of the second corpus;
• creating, by the second neural network and for a keyword in the second corpus, a keyword vector, wherein the keyword vector is a multi-dimensional vector that defines the keyword based in part on a relationship of the keyword to at least a portion of the remainder of the second corpus;
• inputting the entity vector and the sentiment score for the entity into a first pattern-recognizer pathway in a third neural network;
• inputting the concept vector into a second pattern-recognizer pathway in the third neural network;
• inputting the keyword vector into a third pattern-recognizer pathway in the third neural network;
• and
• generating, by the third neural network and based on the entity vector, the concept vector, the keyword vector, and the sentiment score, a probability value of the potential future event that is associated with the identified subject matter.