10. A method of analyzing feedback data of an electronic learning system comprising: receiving a plurality of feedback data from one or more client devices, the received feedback data corresponding to user feedback of one or more users relating to electronic learning content of the electronic learning system; determining an associated user and a sentiment score for each of the received plurality of feedback data; grouping the plurality of feedback data into one or more feedback aggregations associated with the one or more users; calculating a sentiment score for each of the one or more first feedback aggregations associated with the one or more users; receiving user records associated with each of the one or more users, the received user records relating to interactions of the one or more users with the electronic learning system occurring after receipt of the feedback data; storing the user records and associated sentiment scores for each of the one or more user within a data store of the electronic learning system; training a machine learning algorithm based on the stored user records and associated sentiment scores, for each of the one or more users within the data store of the electronic learning system; receiving additional feedback data from the one or more client devices, the additional feedback data including user feedback from a first user relating to electronic learning content; calculating a sentiment score for the first user, based on the received additional feedback data; using the stored user records and associated sentiment scores in the data store of the electronic learning system, determining a user record prediction for the first user using the trained machine learning algorithm, based on the calculated sentiment score for the first user; determining an output and one or more output devices, based on the determined user record prediction for the first user; and transmitting the determined output to the determined one or more output devices.
10. A method of analyzing feedback data of an electronic learning system comprising: receiving a plurality of feedback data from one or more client devices, the received feedback data corresponding to user feedback of one or more users relating to electronic learning content of the electronic learning system; determining an associated user and a sentiment score for each of the received plurality of feedback data; grouping the plurality of feedback data into one or more feedback aggregations associated with the one or more users; calculating a sentiment score for each of the one or more first feedback aggregations associated with the one or more users; receiving user records associated with each of the one or more users, the received user records relating to interactions of the one or more users with the electronic learning system occurring after receipt of the feedback data; storing the user records and associated sentiment scores for each of the one or more user within a data store of the electronic learning system; training a machine learning algorithm based on the stored user records and associated sentiment scores, for each of the one or more users within the data store of the electronic learning system; receiving additional feedback data from the one or more client devices, the additional feedback data including user feedback from a first user relating to electronic learning content; calculating a sentiment score for the first user, based on the received additional feedback data; using the stored user records and associated sentiment scores in the data store of the electronic learning system, determining a user record prediction for the first user using the trained machine learning algorithm, based on the calculated sentiment score for the first user; determining an output and one or more output devices, based on the determined user record prediction for the first user; and transmitting the determined output to the determined one or more output devices. 13. The method of claim 10 , wherein the received plurality of feedback is related to a presentation of live electronic learning content within the electronic learning system, and wherein determining and providing the output to the one or more output devices comprises: determining one or more computing devices associated with a presenter of the live electronic learning content; determining the output for the presenter of the live electronic learning content, based on the sentiment score for the a feedback aggregation; and transmitting the output to the computing devices associated with the presenter of the live electronic learning content during the presentation of live electronic learning content.