SOCIAL NETWORK MESSAGE CATEGORIZATION SYSTEMS AND METHODS

• US 20100293170A1
• Filed: 05/17/2010
• Published: 11/18/2010
• Est. Priority Date: 05/15/2009
• Status: Active Grant

• Current Assignee: Citizennet Inc. (Advance Publications Incorporated)
• Original Assignee: Citizennet Inc. (Advance Publications Incorporated)
• Inventors: Benyamin, Daniel, Hall, Michael Aaron, Chu, Aaron
• Granted Patent: US 8,504,550 B2
• Time in Patent Office: Days
• US Class Current: 707/750
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

Systems and methods of identifying and categorizing social network messages that are relevant to selected categories and text terms are provided. The frequency of text terms appearing in social network messages are calculated for multiple categories. Based on the calculated text term frequency, social network messages can be identified and/or categorized that match a provided set of text terms. Selecting and/or associating text terms and categories are determined by repeatedly analyzing social network messages.

Claims

1. A method of textual categorization of social network messages, the method comprising:

scoring one or more messages from one or more social network messaging services based on one or more text terms for a determined category by a message server;

matching the one or more scored messages to one or more text terms from a query; and

returning one or more messages having a final score that equals or exceeds a threshold value for the determined category, the final score being based on scores of the scored messages and match values of the matched messages.

2. The method of claim 1 further comprising scoring the query based on one or more text terms associated with one or more categories by the message server to identify the determined category.

3. The method of claim 1 further comprising retrieving the one or more text terms associated with one or more categories from a message database.

4. The method of claim 1 wherein the text terms are unambiguous text terms.

5. The method of claim 4 further comprising testing ambiguity of a text term to generate unambiguous text terms.

6. The method of claim 4 further comprising matching text terms to a text database to generate unambiguous text terms.

7. The method of claim 4 further comprising establishing one or more categories by the message server through matching text terms to a category database.

8. The method of claim 7 further comprising associating the unambiguous text terms to the established categories by the message server.

9. The method of claim 8 further comprising adding or removing one or more text terms to the established categories.

10. The method of claim 1 wherein scoring one or more messages is based on text term frequency calculations associated with the one or more text terms.

11. The method of claim 10 further comprising counting a number of occurrences of a text term relative to a plurality of messages from one or more social messaging services to determine the text term frequency calculations for the text term.

12. The method of claim 11 further comprising attenuating the counts for the one or more text terms.

13. The method of claim 12 further comprising normalizing the attenuated counts for the one or more text terms.

14. The method of claim 12 further comprising adjusting the text term frequency calculations.

15. The method of claim 14 wherein adjusting the text term frequency calculations further comprises repeatedly counting a number of occurrences of a text term relative to a plurality of messages from one or more social messaging services.