SCALABLE SYSTEMS AND METHODS FOR CURATING USER EXPERIENCE TEST RESULTS

1 . A method comprising:

identifying, by at least one process executing on at least one hardware processor, a qualitative element within a result set for a user experience test;

selecting, by the at least one process, at least one trained machine learning model to apply to the qualitative element based at least in part on one or more attributes associated with the user experience test;

generating, by the at least on process using the at least one trained machine learning model that was selected, at least one of a predicted visibility, quality, or relevance of the qualitative element; and

generating, based at least in part on at least one of the predicted visibility, quality, or relevance of the qualitative element, a user interface associated with the result set for the user experience test.

2 . The method of claim 1 , further comprising: training a plurality of machine learning models using a set of training examples that are specific to different domains.

3 . The method of claim 2 , wherein the plurality of machine learning models includes at least one neural language model that is trained to predict visibility for quotations based on patterns learned from the set of training examples that are specific to a corresponding domain mapped to the neural language model.

4 . The method of claim 1 , further comprising: generating, by the at least one process using a set of one or more rules, a second prediction for the qualitative element, wherein the user interface is further generated based at least in part on the second prediction for the qualitative element.

5 . The method of claim 1 , further comprising: assigning, by the at least one process, a flag to the qualitative element based on one or more keywords included in the qualitative element, wherein the user interface is further generated based at least in part on the flag assigned to the qualitative element.

6 . The method of claim 1 , further comprising: receiving feedback associated with the predicted visibility, quality, or relevance of the qualitative element; and responsive to receiving the feedback, tuning one or more parameters of the at least one machine least one trained machine learning model.

7 . The method of claim 1 , further comprising: periodically retraining the at least one machine learning model; wherein examples older than a threshold age are filtered from a training dataset used to retrain the at least one machine learning model.

8 . The method of claim 1 , wherein the qualitative element is a response to a survey question about a user experience; wherein the one or more attributes are determined based at least in part on the survey question.

9 . The method of claim 1 , wherein the one or more attributes are determined based at least in part on a target segment for the user experience test.

10 . The method of claim 1 , wherein the one or more attributes are determined based at least in part on a particular facet of a user experience for which the qualitative element was submitted.

11 . One or more non-transitory computer-readable media storing instructions which, when executed by one or more hardware processors cause:

identifying, by at least one process, a qualitative element within a result set for a user experience test;

selecting, by the at least one process, at least one trained machine learning model to apply to the qualitative element based at least in part on one or more attributes associated with the user experience test;

generating, by the at least on process using the at least one trained machine learning model that was selected, at least one of a predicted visibility, quality, or relevance of the qualitative element; and

generating, based at least in part on at least one of the predicted visibility, quality, or relevance of the qualitative element, a user interface associated with the result set for the user experience test.

12 . The media of claim 11 , wherein the instructions further cause: training a plurality of machine learning models using a set of training examples that are specific to different domains.

13 . The media of claim 12 , wherein the plurality of machine learning models includes at least one neural language model that is trained to predict visibility for quotations based on patterns learned from the set of training examples that are specific to a corresponding domain mapped to the neural language model.

14 . The media of claim 11 , wherein the instructions further cause: generating, by the at least one process using a set of one or more rules, a second prediction for the qualitative element, wherein the user interface is further generated based at least in part on the second prediction for the qualitative element.

15 . The media of claim 11 , wherein the instructions further cause: assigning, by the at least one process, a flag to the qualitative element based on one or more keywords included in the qualitative element, wherein the user interface is further generated based at least in part on the flag assigned to the qualitative element.

16 . The media of claim 11 , wherein the instructions further cause: receiving feedback associated with the predicted visibility, quality, or relevance of the qualitative element; and responsive to receiving the feedback, tuning one or more parameters of the at least one machine least one trained machine learning model.

17 . The media of claim 11 , wherein the instructions further cause: periodically retraining the at least one machine learning model; wherein examples older than a threshold age are filtered from a training dataset used to retrain the at least one machine learning model.

18 . The media of claim 11 , wherein the qualitative element is a response to a survey question about a user experience; wherein the one or more attributes are determined based at least in part on the survey question.

19 . The media of claim 11 , wherein the one or more attributes are determined based at least in part on a target segment for the user experience test.

20 . A system comprising:

one or more hardware processors;

one or more non-transitory computer-readable media storing instructions which,

when executed by the one or more hardware processors cause:

identifying, by at least one process, a qualitative element within a result set for a user experience test;

selecting, by the at least one process, at least one trained machine learning model to apply to the qualitative element based at least in part on one or more attributes associated with the user experience test;

generating, by the at least on process using the at least one trained machine learning model that was selected, at least one of a predicted visibility, quality, or relevance of the qualitative element; and

generating, based at least in part on at least one of the predicted visibility, quality, or relevance of the qualitative element, a user interface associated with the result set for the user experience test.