Sas Institute Inc.

Thomas Robert Lehman

John Clare Brocklebank

Bruce Monroe Mills

Heather Michelle GoodyKoontz

John Haws

Balaji Gopalakrishnan

William Elliot Inman

Jody Porowski

Michael T Brooks

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.

Computer-implemented systems and methods for taxonomy development

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.

Computer-implemented systems and methods for mood state determination

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.

In accordance with the teachings described herein, systems and methods are provided for optimizing distribution of advertisement information. In one example, call tracking data may be generated from a plurality of telephone calls made to a business entity, where the call tracking data includes geographical information to identify locations from which the plurality of telephone calls originated. A call distribution may be determined from the call tracking data, where the call distribution groups the call tracking data based at least in part on distances between the business entity and the locations from which the plurality of telephone calls originated. A probability density function may be generated from the call distribution, where the probability density function is for determining a probability that a telephone call will be received by the business entity in response to advertisement information delivered to a call location, and wherein the probability density function expresses the probability as a function of distance between the call location and the business entity. The probability density function may then be used in the generation of the advertisement distribution plan.

Systems and methods for optimizing distribution of advertisement information

In accordance with the teachings described herein, systems and methods are provided for optimizing distribution of advertisement information. In one example, call tracking data may be generated from a plurality of telephone calls made to a business entity, where the call tracking data includes geographical information to identify locations from which the plurality of telephone calls originated. A call distribution may be determined from the call tracking data, where the call distribution groups the call tracking data based at least in part on distances between the business entity and the locations from which the plurality of telephone calls originated. A probability density function may be generated from the call distribution and used in the generation of the advertisement distribution plan.

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Holly Springs, NC, United States of America

Thomas Robert Lehman