Abstract: A method implements content selection using psychological factors. A person vector is generated from a record. The person vector identifies a set of data representing a person. A psychological factor vector is generated from a content of a plurality of content. The psychological factor vector identifies a set of psychological factors from the content. The person vector and the psychological factor vector are combined to generate an input vector. An action score is generated from the input vector. The action score identifies a probability of an action on the content by the person. The content is selected from the plurality of content using the action score. The content is presented.

Abstract: A method implements content selection using psychological factors. A person vector is generated from a record. The person vector identifies a set of data representing a person. A psychological factor vector is generated from a content of a plurality of content. The psychological factor vector identifies a set of psychological factors from the content. The person vector and the psychological factor vector are combined to generate an input vector. An action score is generated from the input vector. The action score identifies a probability of an action on the content by the person. The content is selected from the plurality of content using the action score. The content is presented.

Abstract: Methods and systems classify content using scoring for identifying psychological factors employed by consumers to take action. An interface module obtains content including one or more elements. One or more feature vectors are extracted from the content. The one or more feature vectors include a feature vector that identifies an element of the one or more elements of the content. A classification scoring module generates one or more classification vectors from the one or more feature vectors. The one or more classification vectors include a classification vector that identifies one or more characteristics of the element from the content. The one or more classification vectors are combined and one or more characteristics of the content are identified to form an aggregated vector. A goal of the content is detected by generating a string that describes the content from the aggregated vector. The goal is presented with the content.

Abstract: An interface module obtains content comprising one or more elements. One or more feature vectors are extracted from the content. The one or more feature vectors comprise a feature vector that identifies an element of the one or more elements of the content. A classification scoring module generates one or more classification vectors from the one or more feature vectors. The one or more classification vectors comprise a classification vector that identifies one or more characteristics of the element from the content. The one or more classification vectors are combined and one or more characteristics of the content are identified to form an aggregated vector. A goal of the content is detected by generating a string that describes the content from the aggregated vector. The goal is presented with the content.

Abstract: An interface module obtains content comprising one or more elements. One or more feature vectors are extracted from the content. The one or more feature vectors comprise a feature vector that identifies an element of the one or more elements of the content. A classification scoring module generates one or more classification vectors from the one or more feature vectors. The one or more classification vectors comprise a classification vector that identifies one or more characteristics of the element from the content. The one or more classification vectors are combined and one or more characteristics of the content are identified to form an aggregated vector. A goal of the content is detected by generating a string that describes the content from the aggregated vector. The goal is presented with the content.

Abstract: An interface module obtains content comprising one or more elements. One or more feature vectors are extracted from the content. The one or more feature vectors comprise a feature vector that identifies an element of the one or more elements of the content. A classification scoring module generates one or more classification vectors from the one or more feature vectors. The one or more classification vectors comprise a classification vector that identifies one or more characteristics of the element from the content. The one or more classification vectors are combined and one or more characteristics of the content are identified to form an aggregated vector. A goal of the content is detected by generating a string that describes the content from the aggregated vector. The goal is presented with the content.