Media content assessment and control systems

• US 8,340,957 B2
• Filed: 02/25/2009
• Issued: 12/25/2012
• Est. Priority Date: 08/31/2006
• Status: Expired due to Fees

• Current Assignee: Waggener Edstrom Worldwide, Inc.
• Original Assignee: Waggener Edstrom Worldwide, Inc.
• Inventors: Gallagher, Daniel Gerard, Lin, Jia, Stoffregen, Marc
• Primary Examiner(s): Hudspeth, David R
• Assistant Examiner(s): SPOONER, LAMONT M
• Application Number:
  US12/392,320
• Publication Number:
  US 20090177463A1
• Time in Patent Office: 1,399 Days
• Field of Search: 704/1, 704/9, 704/10, 707706-708
• US Class Current: 704/9
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing

Abstract

Computer implemented methods, computing devices, and computing systems, wherein relationships of words or phrases within a textual corpus are assessed via frequencies of occurrence of particular words or phrases and via frequencies of co-occurrence of particular pairs of words or phrases within defined tracts of text from within the textual corpus.

Claims

1. A computer-implemented method of adapting a characterized textual corpus state to a target state comprising:

deriving from a textual corpus an assessed textual corpus state on a physical computing device comprising;

parsing the textual corpus and filtering the parsed textual corpus yielding the assessed textual corpus state, the assessed textual corpus state comprising;

a set of derived keywords;

each derived keyword including a subset comprising an associated derived keyword frequency of occurrence within the defined textual corpus;

a set of high-frequency words;

each high-frequency word including an associated high-frequency word frequency of occurrence within the defined textual corpus;

a set of weighted frequencies of within-sentence co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from a combined set of words comprising the set of derived keywords and the set of high-frequency words; and

a set of weighted frequencies of within-paragraph co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from the combined set of words;

providing the target state of the textual corpus comprising;

a set of initial keywords;

each initial keyword including a subset comprising an associated initial keyword frequency of occurrence from within the defined textual corpus;

a set of frequencies of within-sentence co-occurrence of pairs of initial keywords from within the defined textual corpus; and

a set of frequencies of within-paragraph co-occurrence of pairs of initial keywords from within the defined textual corpus;

constructing a weighted adjacency matrix comprising the derived keyword frequency subset and a weighted co-occurrence pair of words, the weighted co-occurrence pair of words comprising the set of weighted frequencies of within-sentence co-occurrence and the set of weighted frequencies of within-paragraph co-occurrence; and

generating on the physical device a difference matrix based on differencing at least one of;

(a) the set of within-sentence co-occurrence of pairs of derived keywords and the provided set of within-sentence co-occurrence of pairs of initial keywords; and

(b) the set of within-paragraph co-occurrence of pairs of derived keywords and the provided set of within-paragraph co-occurrence of pairs of initial keywords.

2. The computer-implemented method of claim 1 further comprising receiving a defined textual corpus comprising at least one textual output from a publisher of a selected set of initial publishers.

3. The computer-implemented method of claim 1 further comprising receiving a set of initial text publishers comprising at least one text publisher.

4. The computer-implemented method of claim 3 further comprising transmitting a textual input for the selected set of initial text publishers based on the generated difference matrix.

5. The computer-implemented method of claim 1 further comprising:

generating a tie strength indicator for each set of pairs of derived keywords based on a frequency count of the set of pairs of derived keywords appearing proximate to one another.

6. The computer-implemented method of claim 1 wherein the weighted frequencies of co-occurrence are applied based on a determined proximity type, the proximity type determined based on proximity of at least one of:

(a) word pair within a defined tract of text;

(b) sentence position of word pair; and

(c) paragraph position of the word pair.

7. The computer-implemented method of claim 1 further comprising:

adapting the characterized textual corpus state to the target state, to effect change in future media output, based on a comparison of the constructed weighted adjacency matrix with the generated difference matrix.

8. The computer-implemented method of claim 1 wherein the generating on the physical device a difference matrix is further based on differencing the derived keyword frequency subset and the provided initial keyword frequency subset.

9. A computer-implemented method of adapting a characterized textual corpus state to a target state comprising:

deriving from a textual corpus an assessed textual corpus state on a first physical computing device comprising;

parsing the textual corpus and filtering the parsed textual corpus yielding the assessed textual corpus state, the assessed textual corpus state comprising;

a set of derived keywords;

each derived keyword including a subset comprising an associated derived keyword frequency of occurrence within the defined textual corpus;

a set of high-frequency words;

each high-frequency word including an associated high-frequency word frequency of occurrence within the defined textual corpus;

a set of weighted frequencies of within-sentence co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from a combined set of words comprising the set of derived keywords and the set of high-frequency words; and

a set of weighted frequencies of within-paragraph co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from the combined set of words;

providing, on at least one of;

the first physical computing device and a second physical computing device, the target state of the textual corpus comprising;

a set of initial keywords;

each initial keyword including a subset comprising an associated initial keyword frequency of occurrence from within the defined textual corpus;

a set of frequencies of within-sentence co-occurrence of pairs of initial keywords from within the defined textual corpus; and

a set of frequencies of within-paragraph co-occurrence of pairs of initial keywords from within the defined textual corpus;

constructing a weighted adjacency matrix comprising the derived keyword frequency subset and a weighted co-occurrence pair of words, the weighted co-occurrence pair of words comprising the set of weighted frequencies of within-sentence co-occurrence and the set of weighted frequencies of within-paragraph co-occurrence; and

generating on the second physical computing device a difference matrix based on differencing at least one of;

(a) the derived keyword frequency subset and the provided initial keyword frequency subset;

(b) the set of within-sentence co-occurrence of pairs of derived keywords and the provided set of within-sentence co-occurrence of pairs of initial keywords; and

(c) the set of within-paragraph co-occurrence of pairs of derived keywords and the provided set of within-paragraph co-occurrence of pairs of initial keywords.

10. The computer-implemented method of claim 9 further comprising receiving, on at least one of:

the first physical computing device and the second physical computing device, a defined textual corpus comprising at least one textual output from a publisher of a selected set of initial publishers.

11. The computer-implemented method of claim 9 further comprising receiving, on at least one of:

the first physical computing device and the second physical computing device, a set of initial text publishers comprising at least one text publisher.

12. The computer-implemented method of claim 11 further comprising transmitting, by at least one of:

the first physical computing device and the second physical computing device, a textual input for the selected set of initial text publishers based on the generated difference matrix.

13. A computing device comprising:

a processing unit and addressable memory,

wherein the processing unit is configured to;

derive from a textual corpus an assessed textual corpus state on a physical computing device comprising;

the execution of one or more instructions to parse the textual corpus, filter the parsed textual corpus, and yield the assessed textual corpus state, the assessed textual corpus comprising;

a set of derived keywords;

each derived keyword including a subset comprising an associated derived keyword frequency of occurrence within the defined textual corpus;

a set of high-frequency words;

each high-frequency word including an associated high-frequency word frequency of occurrence within the defined textual corpus;

a set of weighted frequencies of within-sentence co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from a combined set of words comprising the set of derived keywords and the set of high-frequency words; and

a set of weighted frequencies of within-paragraph co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from the combined set of words;

provide the target state of the textual corpus, the target state comprising;

a set of initial keywords;

each initial keyword including a subset comprising an associated initial keyword frequency of occurrence from within the defined textual corpus;

a set of frequencies of within-sentence co-occurrence of pairs of initial keywords from within the defined textual corpus; and

a set of frequencies of within-paragraph co-occurrence of pairs of initial keywords from within the defined textual corpus;

construct a weighted adjacency matrix comprising the derived keyword frequency subset and a weighted co-occurrence pair of words, the weighted co-occurrence pair of words comprising the set of weighted frequencies of within-sentence co-occurrence and the set of weighted frequencies of within-paragraph co-occurrence; and

generate a difference matrix based on differencing at least one of;

(a) the derived keyword frequency subset and the provided initial keyword frequency subset;

(b) the set of within-sentence co-occurrence of pairs of derived keywords and the provided set of within-sentence co-occurrence of pairs of initial keywords; and

(c) the set of within-paragraph co-occurrence of pairs of derived keywords and the provided set of within-paragraph co-occurrence of pairs of initial keywords.

14. The computing device of claim 13 wherein the processing unit is further configured to receive a defined textual corpus comprising at least one textual output from a publisher of a selected set of initial publishers.

15. The computing device of claim 13 wherein the processing unit is further configured to receive a set of initial text publishers comprising at least one text publisher.

16. The computing device of claim 15 wherein the processing unit is further configured to transmit a textual input for the selected set of initial text publishers based on the generated difference matrix.