The present invention generally relates to an improved system and method for providing email classification. Specifically, the present invention relates to an email classification system and method for analyzing the signature of an email for proper classification.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for providing simplified end-to-end security for computing devices in standalone, LAN, WAN or Internet architectures; said system comprising: an email processing module, comprising computer-executable code stored in non-volatile memory, a machine learning module, comprising computer-executable code stored in non-volatile memory, a processor, and a communications means, wherein said email processing module, said machine learning module, said processor, and said communications means are operably connected and are configured to: receive an email; remove hypertext markup language (HTML) from said email; remove white space, new line, carriage returns (CR) and tabs from said email; convert all text contained in said email to lowercase characters; compare text to relationship terms stored in a relationship term database; tag text matching one or more of said relationship terms; tag text comprising dates, numbers, indicators of time, measurement units, and currency symbols; tag text comprising parts of speech; compare text to lemmatize terms stored in a lemmatize dictionary database; tag text matching one or more lemmatize terms; remove non-essential punctuation from said text; calculate and weigh term frequency in said text using term frequency inverse document frequency; eliminate one or more terms with the lowest calculated weight; and classify said email based on remaining tags and terms. 2. The system of claim 1, wherein the classification of said email is accomplished via a Naive Bayes classifier process. 3. The system of claim 1, wherein the system further comprises a NaïBayes Trainer module and a NaïBayes classifier module. 4. The system of claim 1, wherein the classification of said email is accomplished via a Support Vector Machines (SVM) or Support Vector Networks (SVN) classifier process. 5. The system of claim 1, wherein the system further comprises one or more of a Support Vector Machine trainer module, a Support Vector Network trainer module, a Support Vector Machine classifier module, and a Support Vector Network classifier module. 6. The system of claim 1, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to match remaining terms with categories stored in a category database. 7. The system of claim 6, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to replace one or more remaining terms with replacement tags. 8. The system of claim 7, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to move said email to a location based on said replacement tags. 9. The system of claim 6, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to replace one or more remaining terms with replacement categories. 10. The system of claim 9, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to move said email to a location based on said replacement categories. 11. A method for classifying emails, said method comprising the steps of: receiving an email at an email processing module, comprising computer-executable code stored in non-volatile memory; removing hypertext markup language (HTML) from said email; removing multiple white space, and tabs from said email; converting all text contained in said email to lowercase characters; comparing text to relationship terms stored in a relationship term database; tagging text matching one or more of said relationship terms; tagging text comprising dates, numbers, indicators of time, measurement units, and currency symbols; tagging text comprising parts of speech; comparing text to lemmatize terms stored in a lemmatize dictionary database; tagging text matching one or more lemmatize terms; removing non-essential punctuation from said text; calculating and weigh term frequency in said text using term frequency inverse document frequency; eliminating one or more terms with the lowest calculated weight; and classifying said email based on remaining tags and terms. 12. The method of claim 11, wherein the classification of said email is accomplished via a Naive Bayes classifier process. 13. The method of claim 11, wherein the classification of said email is accomplished via a Support Vector Machines (SVM) or Support Vector Networks (SVN) classifier process. 14. The method of claim 11, further comprising the step of matching remaining terms with categories stored in a category database. 15. The method of claim 11, further comprising the step of replacing one or more remaining terms with replacement tags. 16. The method of claim 15, further comprising the step of moving said email to a location based on said replacement tags. 17. The method of claim 11, further comprising the step of replacing one or more remaining terms with replacement categories. 18. The method of claim 17, further comprising the step of moving said email to a location based on said replacement categories.