Polarity semantics engine analytics platform
Embodiments of the systems and methods disclosed herein provide a prescriptive analytics platform, a polarity analysis engine, and a semantic 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. The systems and methods may also include a semantic analysis engine to extend the results of the polarity analysis engine for improved determination of predictive factors and improved recommendations.
1. A polarity semantics engine analytics system, the system comprising:
a first electronic database storing a set of response data, the set of response data comprising a structured data set and free text data, and wherein the set of response data is based at least partly on an aggregated customer feedback data set;
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; and
a hardware processor is configured to execute computer-executable instructions in order to:
access a correlated structured data set based at least in part on key predictive factors that are correlated to the first objective and indicate one or more behavior patterns associated with the first objective;
access a predictive model, wherein the predictive model is based at least in part on the structured data set and the first objective, wherein the predictive model indicates one or more behavior patterns associated with an objective;
automatically generate a first electronic graph data dependency structure based at least in part on the predictive model, wherein the first electronic graph data dependency structure represents relationships among at least a portion of variables of the correlated structured data set, and wherein the relationships are based at least in part on the strength of an association among each of the variables of the correlated structured data set;
access polarity values associated with at least a portion of the free text data set;
automatically generate a second electronic graph data dependency structure of associations among model inputs and polarity values associated with at least a portion of the free text data, wherein the polarity values indicate that the associated free text data is associated with a degree of impact on one or more outcomes;
automatically generate an extended electronic graph data dependency structure based at least in part on the first and second electronic graph data dependency structures; and
automatically generate a recommendation action based at least in part on the first objective and the extended electronic graph data dependency structure.
2. The system of claim 1 , wherein the hardware processor is configured to execute computer-executable instructions in order to determine associative or causal interpretations of discovered relationships between each of the variables of the correlated structured data set.
3. The system of claim 2 , wherein the recommendation is based at least in part on the associative or causal interpretations, wherein the recommendation includes actions associated with text within the aggregated customer feedback data set to be implemented to achieve the first objective, and wherein the actions reduce negative feedback or increase positive feedback.
4. The system of claim 1 , wherein the hardware processor is configured to execute computer-executable instructions in order to generate a data packet that includes a graphical representation of at least a subset of the polarity values that includes a graphical representation of the extended graph, the data packet configured for display on a remote computing device.
5. The system of claim 1 , wherein the hardware processor is configured to execute computer-executable instructions in order to generate a data packet that includes a graphical representation of at least a subset of the polarity values that includes a graphical representation of whether a term in the at least a subset is tied to a negative sentiment or a positive sentiment, the data packet configured for display on a remote computing device.
6. The system of claim 1 , wherein the hardware processor is configured to execute computer-executable instructions in order to generate a data packet that includes a graphical representation of at least a subset of the polarity values that includes a graphical representation of the frequency and strength of terms in the subset, the data packet configured for display on a remote computing device.
7. The system of claim 1 , wherein the hardware processor is configured to execute computer-executable instructions in order to generate a data packet that includes a graphical representation of at least a subset of the polarity values that includes a graphical representation of whether a term in the subset is tied to a negative sentiment or a positive sentiment and the frequency and strength of terms in the subset, the data packet configured for display on a remote computing device.
8. A computer-implemented method comprising:
accessing from a first electronic database 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 data set;
accessing from a second electronic database a first objective corresponding to the set of response data, the first objective is associated with one or more objective values;
accessing a correlated structured data set based at least in part on key predictive factors that are correlated to the first objective and indicate one or more behavior patterns associated with the first objective;
accessing a predictive model, wherein the predictive model is based at least in part on the structured data set and the first objective, wherein the predictive model indicates one or more behavior patterns associated with an objective;
automatically generating a first electronic graph data dependency structure based at least in part on the predictive model, wherein the first electronic graph data dependency structure represents relationships among at least a portion of variables of the correlated structured data set, and wherein the relationships are based at least in part on the strength of an association among each of the variables of the correlated structured data set;
accessing polarity values associated with at least a portion of the free text data set;
automatically generating a second electronic graph data dependency structure of associations among model inputs and polarity values associated with at least a portion of the free text data, wherein the polarity values indicate that the associated free text data is associated with a degree of impact on one or more outcomes;
automatically generating an extended electronic graph data dependency structure based at least in part on the first and second electronic graph data dependency structures; and
automatically generating a recommendation action based at least in part on the first objective and the extended electronic graph data dependency structure.
9. The computer-implemented method of claim 8 , further comprising:
determining associative or causal interpretations of discovered relationships between each of the variables of the correlated structured data set.
10. The computer-implemented method of claim 9 , wherein the recommendation is based at least in part on the associative or causal interpretations, wherein the recommendation includes actions associated with text within the aggregated customer feedback data set to be implemented to achieve the first objective, and wherein the actions reduce negative feedback or increase positive feedback.
11. The computer-implemented method of claim 8 , further comprising:
generating a data packet that includes a graphical representation of at least a subset of the plurality of lemmas that includes a graphical representation of the extended graph, the data packet configured for display on a remote computing device.
12. The computer-implemented method of claim 8 , further comprising:
generating a data packet that includes a graphical representation of at least a subset of the polarity values that includes a graphical representation of whether a term in the at least a subset is tied to a negative sentiment or a positive sentiment, the data packet configured for display on a remote computing device.
13. The computer-implemented method of claim 8 , further comprising:
generating a data packet that includes a graphical representation of at least a subset of the polarity values that includes a graphical representation of the frequency and strength of terms in the subset, the data packet configured for display on a remote computing device.
14. The computer-implemented method of claim 8 , further comprising:
generating a data packet that includes a graphical representation of at least a subset of the polarity values that includes a graphical representation of whether a term in the subset is tied to a negative sentiment or a positive sentiment and the frequency and strength of terms in the subset.
15. Non-transitory computer storage having stored thereon a computer program, the computer program including executable instructions that instruct a computer system to at least:
access from a first electronic database 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 data set;
access from a second electronic database a first objective corresponding to the set of response data, the first objective is associated with one or more objective values;
access a correlated structured data set based at least in part on key predictive factors that are correlated to the first objective and indicate one or more behavior patterns associated with the first objective;
access a predictive model, wherein the predictive model is based at least in part on the structured data set and the first objective, wherein the predictive model indicates one or more behavior patterns associated with an objective;
automatically generate a first electronic graph data dependency structure based at least in part on the predictive model, wherein the first electronic graph data dependency structure represents relationships among at least a portion of variables of the correlated structured data set, and wherein the relationships are based at least in part on the strength of an association among each of the variables of the correlated structured data set;
access polarity values associated with at least a portion of the free text data set;
automatically generate a second electronic graph data dependency structure of associations among model inputs and polarity values associated with at least a portion of the free text data, wherein the polarity values indicate that the associated free text data is associated with a degree of impact on one or more outcomes;
automatically generate an extended electronic graph data dependency structure based at least in part on the first and second electronic graph data dependency structures; and
automatically generate a recommendation action based at least in part on the first objective and the extended electronic graph data dependency structure.
16. The non-transitory computer storage of claim 15 , further comprising:
determine associative or causal interpretations of discovered relationships between each of the variables of the correlated structured data set.
17. The non-transitory computer storage of claim 16 , wherein
the recommendation is based at least in part on the associative or causal interpretations, wherein the recommendation includes actions associated with text within the aggregated customer feedback data set to be implemented to achieve the first objective, and wherein the actions reduce negative feedback or increase positive feedback.
18. The non-transitory computer storage of claim 15 , further comprising:
generate a data packet that includes a graphical representation of at least a subset of the plurality of lemmas that includes a graphical representation of the extended graph, the data packet configured for display on a remote computing device.
19. The non-transitory computer storage of claim 15 , further comprising:
generate a data packet that includes a graphical representation of at least a subset of the plurality of lemmas that includes a graphical representation of whether a term in the at least a subset is tied to a negative sentiment or a positive sentiment, the data packet configured for display on a remote computing device.
20. The non-transitory computer storage of claim 15 , further comprising:
generate a data packet that includes a graphical representation of at least a subset of the polarity values that includes a graphical representation of the frequency and strength of terms in the subset, the data packet configured for display on a remote computing device.
21. The non-transitory computer storage of claim 15 , further comprising:
generate a data packet that includes a graphical representation of at least a subset of the polarity values that includes a graphical representation of whether a term in the subset is tied to a negative sentiment or a positive sentiment and the frequency and strength of terms in the subset.