Media content assessment and control systems
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.
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.