Systems and methods for using vector model normal exclusion in natural language processing to characterize a category of messages

A non-transitory computer-readable storage medium embodying a set of instructions, which when executed by a processing resource, causes the processing resource to:

• determine a set of normal exclusion values by for each unique word in each model document of a plurality of model documents that relate to a particular topic, calculating a normal exclusion value for inclusion within the set of normal exclusion values based on a frequency of the unique word in the model document and a frequency of the unique word in a dictionary, wherein each of the plurality of model documents includes text content;
• create a plurality of model document vectors by forming for a given model document of the plurality of model documents a vector having a plurality of normal exclusion values from the set of normal exclusion values in which each of the plurality of normal exclusion values correspond to a given unique word in the given model document;
• identify a largest value in a first dimension within the plurality of model document vectors as a first topic extreme, and a largest value in a second dimension within the plurality of model document vectors as a second topic extreme;
• and
• categorize a document at issue as included within or excluded from the particular topic by creating a vector for the document at issue based on a model vector definition and comparing the vector to one or both of the first topic extreme and the second topic extreme.

A system comprising:

• a processing resource;
• and
• a non-transitory computer-readable medium, coupled to the processing resource, having stored therein instructions that when executed by the processing resource cause the processing resource to:
• determine a set of normal exclusion values by for each unique word in each model document of a plurality of model documents that relate to a particular topic, calculating a normal exclusion value for inclusion within the set of normal exclusion values based on a frequency of the unique word in the model document and a frequency of the unique word in a dictionary, wherein each of the plurality of model documents includes text content;
• create a plurality of model document vectors by forming for a given model document of the plurality of model documents a vector having a plurality of normal exclusion values from the set of normal exclusion values in which each of the plurality of normal exclusion values correspond to a given unique word in the given model document;
• identify a largest value in a first dimension within the plurality of model document vectors as a first topic extreme, and a largest value in a second dimension within the plurality of model document vectors as a second topic extreme;
• and
• categorize a document at issue as included within or excluded from the particular topic by creating a vector for the document at issue based on a model vector definition and comparing the vector to one or both of the first topic extreme and the second topic extreme.