Prescriptive analytics platform and polarity analysis engine
First Claim
1. A prescriptive analytics system for conducting a polarity analysis on unstructured, free text data, the prescriptive analytics system comprising:
- a first electronic database storing a set of response data, the set of response data comprising a structured data set and a free text data set, and wherein the set of response data is based at least partly on an aggregated customer feedback dataset;
a second electronic database storing a first objective corresponding to the set of response data, the first objective selected by a user, wherein the first objective is associated with one or more objective values such that each objective value is associated with a degree of impact on one or more outcomes, wherein the one or more outcomes are based at least partly on the first objective selected by the user;
a hardware processor is configured to execute computer-executable instructions in order to;
access from the first electronic database the set of response data;
analyze the structured data set of the structured data to identify key predictive factors within the structured data set that are correlated to the first objective and indicate one or more behavior patterns associated with the first objective;
generate a correlated structured data set from the structured data set using the identified key predictive factors;
convert a plurality of terms within the free text data set of the response data into a plurality of lemmas corresponding to the plurality of terms to generate a standardized lemma data set;
filter the standardized lemma data set to remove terms that are not associated with actionable words to create a filtered lemma data set, wherein actionable words include nouns and adjectives selected by the user;
aggregate the filtered lemma data set with the correlated structured data set;
generate scores for the filtered lemma data set in view of the first objective to create a scored lemma data set;
analyze the distribution of the scored lemma data set against a distribution of the first objective in the correlated structured data set to determine a first number of occurrences of lemmas of the plurality of lemmas associated with a positive objective value and to determine a second number of occurrences of lemmas of the plurality of lemmas associated with a negative objective value;
balance the distribution of the scored lemma data set against aggregate frequency of terms in the scored lemma data set to create a distributed lemma data set; and
assign polarity values to terms in the distributed lemma data set, wherein the polarity values indicate whether each of the terms is a positive term or a negative term, based at least partly on a determination of the terms as being associated with a degree of impact on one or more outcomes;
generate a recommendation action based at least in part on the polarity values.
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Abstract
Embodiments of the systems and methods disclosed herein provide a prescriptive analytics platform and polarity analysis engine in which a user can identify a target objective and use the system to find out whether the user'"'"'s objectives are being met, what predictive factors are positively or negatively affecting the targeted objectives, as well as what recommended changes the user can make to better meet the objectives. The systems and methods may include a polarity analysis engine configured to determine the polarity of terms in free-text input in view of the target objective and the predictive factors and use the polarity to generate the recommended changes.
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Citations
24 Claims
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1. A prescriptive analytics system for conducting a polarity analysis on unstructured, free text data, the prescriptive analytics system comprising:
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a first electronic database storing a set of response data, the set of response data comprising a structured data set and a free text data set, and wherein the set of response data is based at least partly on an aggregated customer feedback dataset; a second electronic database storing a first objective corresponding to the set of response data, the first objective selected by a user, wherein the first objective is associated with one or more objective values such that each objective value is associated with a degree of impact on one or more outcomes, wherein the one or more outcomes are based at least partly on the first objective selected by the user; a hardware processor is configured to execute computer-executable instructions in order to; access from the first electronic database the set of response data; analyze the structured data set of the structured data to identify key predictive factors within the structured data set that are correlated to the first objective and indicate one or more behavior patterns associated with the first objective; generate a correlated structured data set from the structured data set using the identified key predictive factors; convert a plurality of terms within the free text data set of the response data into a plurality of lemmas corresponding to the plurality of terms to generate a standardized lemma data set; filter the standardized lemma data set to remove terms that are not associated with actionable words to create a filtered lemma data set, wherein actionable words include nouns and adjectives selected by the user; aggregate the filtered lemma data set with the correlated structured data set; generate scores for the filtered lemma data set in view of the first objective to create a scored lemma data set; analyze the distribution of the scored lemma data set against a distribution of the first objective in the correlated structured data set to determine a first number of occurrences of lemmas of the plurality of lemmas associated with a positive objective value and to determine a second number of occurrences of lemmas of the plurality of lemmas associated with a negative objective value; balance the distribution of the scored lemma data set against aggregate frequency of terms in the scored lemma data set to create a distributed lemma data set; and assign polarity values to terms in the distributed lemma data set, wherein the polarity values indicate whether each of the terms is a positive term or a negative term, based at least partly on a determination of the terms as being associated with a degree of impact on one or more outcomes; generate a recommendation action based at least in part on the polarity values. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer-implemented method of conducting a polarity analysis on unstructured, free text data, the computer-implemented method comprising:
as implemented by one or more computing devices configured with specific computer-executable instructions, accessing from a first electronic database a set of response data that includes a structured data set and an unstructured data set, wherein the set of response data is based at least partly on an aggregated customer feedback dataset; analyzing the structured data set to identify key predictive factors within the structured data set that are correlated to a first objective and indicate one or more behavior patterns associated with the first objective, wherein the first objective corresponds to the set of response data and is selected by a user, and wherein the first objective is associated with one or more objective values such that each objective value is associated with a degree of impact on one or more outcomes, wherein the one or more outcomes are based at least partly on the first objective selected by the user; generating a correlated structured data set from the structured data set using the identified key predictive factors; converting a plurality of terms within the free text data set of the response data into a plurality of lemmas corresponding to the plurality of terms to generate a standardized lemma data set; filtering the standardized lemma data set to remove terms that are not associated with actionable words to create a filtered lemma data set, wherein actionable words include nouns and adjectives selected by the user; aggregating the filtered lemma data set with the correlated structured data set; generating scores for the filtered lemma data set in view of the first objective to create a scored lemma data set; analyzing the distribution of the scored lemma data set against a distribution of the first objective in the correlated structured data set to determine a first number of occurrences of lemmas of the plurality of lemmas associated with a positive objective value and to determine a second number of occurrences of lemma of the plurality of lemmas associated with a negative objective value; balancing the distribution of the scored lemma data set against rarity and aggregate frequency of terms in the scored lemma data set to create a distributed lemma data set; and assigning polarity values to terms in the distributed lemma data set, wherein the polarity values indicate whether each of the terms is a positive term or a negative term, based at least partly on a determination of the terms as being associated with a degree of impact on one or more outcomes; generating a recommendation action based at least in part on the polarity values. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. Non-transitory computer storage having stored thereon a computer program, the computer program including executable instructions that instruct a computer system to at least:
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access from a first electronic database a set of response data that includes a structured data set and an unstructured data set, wherein the set of response data is based at least partly on an aggregated customer feedback dataset; analyze the structured data set to identify key predictive factors within the structured data set that are correlated to a first objective and indicate one or more behavior patterns associated with the first objective, wherein the first objective corresponds to the set of response data and is selected by a user, and wherein the first objective is associated with one or more objective values such that each objective value is associated with a degree of impact on one or more outcomes, wherein the one or more outcomes are based at least partly on the first objective selected by the user; generate a correlated structured data set from the structured data set using the identified key predictive factors; convert a plurality of terms within the free text data set of the response data into a plurality of lemmas corresponding to the plurality of terms to generate a standardized lemma data set; filter the standardized lemma data set to remove terms that are not associated with actionable words to create a filtered lemma data set, wherein actionable words include nouns and adjectives selected by the user; aggregate the filtered lemma data set with the correlated structured data set; generate scores for the filtered lemma data set in view of the first objective to create a scored lemma data set; analyze the distribution of the scored lemma data set against a distribution of the first objective in the correlated structured data set to determine a first number of occurrences of lemmas of the plurality of lemmas associated with a positive objective value and to determine a second number of occurrences of lemma of the plurality of lemmas associated with a negative objective value; balance the distribution of the scored lemma data set against rarity and aggregate frequency of terms in the scored lemma data set to create a distributed lemma data set; and assign polarity values to terms in the distributed lemma data set, wherein the polarity values indicate whether each of the terms is a positive term or a negative term based at least partly on a determination of the terms as being associated with a degree of impact on one or more outcomes; generate a recommendation action based at least in part on the polarity values. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24)
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Specification