Systems and methods for classifying electronic documents

A method, comprising:

• generating, by at least one processor, statistical data from one or more training documents stored in random access memory (RAM) comprising at least one of blogs, messages, and emails, the one or more training documents comprising positive or negative discussions regarding one of a brand, product, and service and filtering the one or more training documents to clean textual content, and creating a plurality of classification rules, including creating at least one topic model-based classification rule using the statistical data, the at least one topic model-based classification rule is formatted as an XML file and stored in RAM;
• evaluating, by the at least one processor, the at least one topic model-based classification rule using a precision equation and a recall equation, the precision equation comprising
• Precision
• =
• N
• ⁡
• (
• d
• c
• +
• )
• N
• ⁡
• (
• d
• c
• +
• )
• +
• N
• ⁡
• (
• d
• c
• -
• )
• and the recall equation comprising
• Recall
• =
• N
• ⁡
• (
• d
• c
• +
• )
• N
• ⁡
• (
• d
• c
• )
• ,
• wherein N(dc+) is a number of test documents correctly classified to a category C based on the at least one topic model-based classification rule, N(dc−) is a number of test documents incorrectly classified to the category C based on the at least one topic model-based classification rule, and N(dc) denotes a number of test documents that should be classified to the category C;
• and
• creating, by the at least one processor, at least one query-based classification rule using one or more user defined categories and the statistical data, the at least one query based classification rule is formatted as an XML file stored in RAM.

A non-transitory computer readable medium having instructions stored thereon that, when executed by at least one processor, cause the at least one processor to perform operations, the operations comprising:

• generating statistical data from one or more training documents stored in random access memory (RAM) comprising at least one of blogs, messages, and emails, the one or more training documents comprising positive or negative discussions regarding one of a brand, product, and service and filtering the one or more training documents to clean textual content, and creating a plurality of classification rules, including creating at least one topic model-based classification rule using the statistical data, the at least one topic model-based classification rule is formatted as an XML file and stored in RAM;
• evaluating the at least one topic model-based classification rule using a precision equation and a recall equation, the precision equation comprising
• Precision
• =
• N
• ⁡
• (
• d
• c
• +
• )
• N
• ⁡
• (
• d
• c
• +
• )
• +
• N
• ⁡
• (
• d
• c
• -
• )
• and the recall equation comprising
• Recall
• =
• N
• ⁡
• (
• d
• c
• +
• )
• N
• ⁡
• (
• d
• c
• )
• ,
• wherein N(dc+) is a number of test documents correctly classified to a category C based on the at least one topic model-based classification rule, N(dc−) is a number of test documents incorrectly classified to the category C based on the at least one topic model-based classification rule, and N(dc) denotes a number of test documents that should be classified to the category C;
• and
• creating at least one query-based classification rule using one or more user defined categories and the statistical data, the at least one query based classification rule is formatted as an XML file stored in RAM.