16. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: 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 presented via an electronic learning system; determining an associated user a feedback context 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 feedback aggregation 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 the receipt of the feedback data; storing the user records and associated sentiment scores for each of the one or more users 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.
16. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: 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 presented via an electronic learning system; determining an associated user a feedback context 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 feedback aggregation 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 the receipt of the feedback data; storing the user records and associated sentiment scores for each of the one or more users 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. 17. The computer-program product as recited in claim 16 , wherein determining the sentiment score for each of the received plurality of feedback data comprises: providing each of the plurality of feedback data to a natural language processing (NLP) neural network; and receiving a plurality of sentiment scores from the NLP neural network corresponding to the received plurality of feedback data.