SHORT MESSAGE AGE CLASSIFICATION

• US 20120259619A1
• Filed: 04/06/2012
• Published: 10/11/2012
• Est. Priority Date: 04/06/2011
• Status: Active Grant

• Current Assignee: Citizennet Inc. (Advance Publications Incorporated)
• Original Assignee: Citizennet Inc. (Advance Publications Incorporated)
• Inventors: Hall, Michael Aaron
• Granted Patent: US 9,063,927 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

Systems and methods for short message age classification in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, classifying messages using a classifier includes determining keyword feature information for a message using the classifier, classifying the determined feature information using the classifier, and estimating user age using the classifier.

Claims

1. A method for classifying messages using a classifier, comprising:

determining keyword feature information for a message using the classifier;

classifying the determined feature information using the classifier; and

estimating user age using the classifier.

2. The method of claim 1, wherein determining keyword feature information for a message includes feature information selected from the group consisting of:

vowel/consonant ratio, capitalization, number of emoticons, punctuation, usage of texting slang, and keyword frequency.

3. The method of claim 2, wherein determining vowel/consonant ratio keyword feature information further comprises:

counting the number of vowels in the message using the classifier;

counting the number of consonants in the message using the classifier; and

dividing the number of vowels in the message by the number of consonants in the message using the classifier.

4. The classifier of claim 3, wherein the letter ‘

y’

is excluded from the number of vowels in the message and from the number of consonants in the message.

5. The method of claim 2, wherein determining capitalization keyword feature information for a message further comprises counting the number of capital letter in the message using the classifier.

6. The method of claim 2, wherein determining emoticon keyword feature information for a message further comprises counting the number of emoticons in the message using the classifier.

7. The method of claim 2, wherein determining punctuation keyword feature information for a message further comprises:

removing emoticons from the message using the classifier; and

counting the number of punctuation marks in the message using the classifier.

8. The method of claim 2, wherein determining usage of texting slang keyword feature information for a message further comprises:

breaking the message into slang tokens using the classifier; and

counting the number of slang tokens using the classifier.

9. The method of claim 8, wherein slang tokens are determined using a dictionary of texting slang and the classifier.

10. The method of claim 2, wherein determining keyword frequency further comprises:

tokenizing a message using the classifier, where the message includes a set of words;

filtering stop words using the tokenized message and the classifier;

keyword stemming the filtered stop words and the classifier; and

counting the stemmed keywords using the classifier.

11. The method of claim 10, wherein tokenizing aggregate user data further comprises:

breaking up the message into word using the classifier, where the words are contained in the set of words; and

creating a token for each word in the broken up message using the classifier.

12. The method of claim 10, wherein the stop words are words in a message which do not provide useful information to the classifier.

13. A classifier, comprising:

memory configured to store aggregate user data, where the aggregate user data includes a set of messages; and

a processor, where the processor is configured to;

determine keyword feature information using aggregate user data;

classify the determined feature information using a classifier application; and

estimate user age using the classified feature information.

14. The classifier of claim 13, wherein the classifier application utilizes a Naï

ve Bayesian classifier.

15. The classifier of claim 13, wherein the classifier application utilizes a maximum entropy classifier.

16. The classifier of claim 13, wherein the processor is further configured to aggregate user data over a network.

17. The classifier of claim 13, wherein the processor is further configured to determine the keyword feature information using the aggregate user data and predetermined feature information.

18. The classifier of claim 13, wherein processor is further configured to determine the keyword feature information for a message contained in the set of messages using the classifier application.

19. The classifier of claim 13, wherein the determined keyword feature information includes feature information selected from the group consisting of:

vowel/consonant ratio, capitalization, number of emoticons, punctuation, usage of texting slang, and keyword frequency.