AUTOMATED TRAINING, RETRAINING AND RELEARNING APPLIED TO DATA ANALYTICS
Systems and methods are provided for data analysis that may be initialized via self-identification from customers and continually trained automatically thereafter. A plurality of records are partitioned into a plurality of tagged sets. The plurality of tagged sets comprises a positive set, a negative set, and a neutral set. A model is generated according to a first portion of the plurality of tagged sets. Then, an initial fit of the model is evaluated according to a second portion of the plurality of tagged sets. The model may then be adjusted according to the initial fit of the model. A final fit of the adjusted model is evaluated according to a third portion of the plurality of tagged sets.
1 - 20 . (canceled)
21 . A method for data analysis, the method comprising:
generating a scale of scores associated with a theme by boosting or suppressing a selected portion of records via a plurality of equalizer sliders;
generating a precision score according to a number of false positives;
generating a recall score according to a number of missing positives;
generating a combined score according to the precision score, the recall score and an inclusion model; and
retaining the inclusion model as a best performing model, wherein:
the inclusion model differentiates inclusion from exclusion on the scale of scores related to the theme, and
the combined score corresponds to a number of records identified according to a tradeoff of precision and recall.
22 . The method of claim 21 , wherein the method comprises:
generating an initial model according to a selective weighting of a first portion of records;
evaluating a fit of the initial model according to a second portion records;
adjusting the initial model according to the fit; and
evaluating an adjusted model according to a third portion of the records.
23 . The method of claim 22 , wherein the method comprises scoring the adjusted model according to a fourth portion of the plurality of tagged sets.
24 . The method of claim 23 , wherein the method comprises comparing a score of the adjusted model to a score of a different model.
25 . The method of claim 24 , wherein the method comprises retaining a better-scoring model according to the comparison.
26 . The method of claim 24 , wherein the method comprises dropping a worse-scoring model according to the comparison.
27 . The method of claim 21 , wherein the method comprises discovering an uptick over time in the combined score related to the theme.
28 . The method of claim 21 , wherein:
the method comprises extracting a key phrase from an article of content associated with the theme,
a number of occurrences of the key phrase exceeds a threshold, and
the key phrase comprises one or more words.
29 . The method of claim 28 , wherein the method comprises comparing the extracted key phrase to a positive set of records.
30 . The method of claim 28 , wherein the method comprises adding the extracted key phrase to a positive set of records.
31 . A system for data analysis, the system comprising:
a plurality of equalizer sliders associated with boosting or suppressing a plurality of selected records; and
a processor configured to:
generate a scale of scores associated with a theme by boosting or suppressing the plurality of selected records according to the plurality of equalizer sliders,
generate a precision score according to a number of false positives,
generate a recall score according to a number of missing positives,
generate a combined score according to the precision score, the recall score and an inclusion model, and
retain the inclusion model as a best performing model, wherein:
the inclusion model differentiates inclusion from exclusion on the scale of scores related to the theme, and
the combined score corresponds to a number of records identified according to a tradeoff of precision and recall.
32 . The system of claim 31 , wherein the processor is configured to:
generate an initial model according to a selective weighting of a first portion of records;
evaluate a fit of the initial model according to a second portion records;
adjust the initial model according to the fit; and
evaluate an adjusted model according to a third portion of the records.
33 . The system of claim 32 , wherein the processor is configured to:
score the adjusted model according to a fourth portion of the plurality of tagged sets.
34 . The system of claim 32 , wherein the processor is configured to:
compare a score of the adjusted model to a score of a different model.
35 . The system of claim 34 , wherein the processor is configured to:
retain a better-scoring model according to the comparison.
36 . The system of claim 34 , wherein the processor is configured to:
drop a worse-scoring model according to the comparison.
37 . The system of claim 31 , wherein the processor is configured to:
discover an uptick over time in the combined score related to the theme.
38 . The system of claim 31 , wherein:
the processor is configured to extract a key phrase from an article of content associated with the theme,
a number of occurrences of the key phrase exceeds a threshold, and
the key phrase comprises one or more words.
39 . The system of claim 38 , wherein the processor is configured to:
compare the extracted key phrase to a positive set of records.
40 . The system of claim 38 , wherein the processor is configured to:
add the extracted key phrase to a positive set of records.