Abstract: A computing device receives training data representing different observations where each observation is categorized into one of options for a target variable. The device obtains computer command(s) for categorizing into one of the options for the target variable. The device generates a sampling scheme for sampling terms of the training data. The device generates sampling models by, for N iterations of the sampling scheme: determining a subset of the training data based on a training data index; sampling, based on a term index, the subset of the training data for a subset of terms; and generating, based on the subset of terms, a sampling model for categorizing, according to the computer command(s). Each sampling model is generated from a different subset of terms such that the sampling models are randomized. The device computes an aggregated model for categorizing test data into one of the options for the target variable.

Abstract: Systems and methods are provided for generating a set of classifiers. A term is identified within a document and a pre-defined threshold distance is determined. A plurality of additional terms in the document are identified, the additional terms being located within the pre-defined threshold distance of the time. A distance between the term and an additional term of the plurality of additional terms is calculated. A corresponding weight for the calculated distance is determined using a proximity weighting scheme. A score for the additional term is calculated using the calculated distance and the corresponding weight. A colocation matrix is generated and a classifier determined using the colocation matrix.

Abstract: Computer-implemented systems and methods are provided for determining an overall mood score of a document. For example, the document is received from a computer-readable medium. A text segment in a document is identified to be indicative of a mood of the document. The text segment is mapped to a mood scale among a predetermined set of mood scales. A mood weight associated with the mood scale for the text segment is generated. An overall mood score of the document is determined based at least in part on the mood weight.

Abstract: Systems and methods are provided for generating a set of classifiers. A term is identified within a document and a pre-defined threshold distance is determined. A plurality of additional terms in the document are identified, the additional terms being located within the pre-defined threshold distance of the time. A distance between the term and an additional term of the plurality of additional terms is calculated. A corresponding weight for the calculated distance is determined using a proximity weighting scheme. A score for the additional term is calculated using the calculated distance and the corresponding weight. A colocation matrix is generated and a classifier determined using the colocation matrix.

Abstract: Systems and methods are provided for generating a set of classifiers. A location is determined for each instance of a topic term in a collection of documents. One or more topic term phrases are identified, and one or more sentiment terms within each topic term phrase. Candidate classifiers are identified by parsing words in the one or more topic term phrases, and a colocation matrix is generated. A seed row of the colocation associated with a particular attribute is identified, and distance metrics are determined by comparing each row of the colocation matrix to the seed row. A set of classifiers are generated for the particular attribute, where classifiers in the set of classifiers are selected using the distance metrics.

Abstract: Computer-implemented systems and methods are provided for determining an overall mood score of a document. For example, the document is received from a computer-readable medium. A text segment in a document is identified to be indicative of a mood of the document. The text segment is mapped to a mood scale among a predetermined set of mood scales. A mood weight associated with the mood scale for the text segment is generated. An overall mood score of the document is determined based at least in part on the mood weight.

Abstract: Computer-implemented systems and methods are provided for determining an overall mood score of a document. For example, the document is received from a computer-readable medium. A text segment in a document is identified to be indicative of a mood of the document. The text segment is mapped to a mood scale among a predetermined set of mood scales. A mood weight associated with the mood scale for the text segment is generated. An overall mood score of the document is determined based at least in part on the mood weight.

Abstract: Computer-implemented systems and methods are provided for determining an overall mood score of a document. For example, the document is received from a computer-readable medium. A text segment in a document is identified to be indicative of a mood of the document. The text segment is mapped to a mood scale among a predetermined set of mood scales. A mood weight associated with the mood scale for the text segment is generated. An overall mood score of the document is determined based at least in part on the mood weight.

Abstract: Systems and methods are provided for generating a set of classifiers. A location is determined for each instance of a topic term in a collection of documents. One or more topic term phrases are identified, and one or more sentiment terms within each topic term phrase. Candidate classifiers are identified by parsing words in the one or more topic term phrases, and a colocation matrix is generated. A seed row of the colocation associated with a particular attribute is identified, and distance metrics are determined by comparing each row of the colocation matrix to the seed row. A set of classifiers are generated for the particular attribute, where classifiers in the set of classifiers are selected using the distance metrics.