1. A sentiment analyzer for an electronic learning system comprising: one or more client devices of the electronic learning system, each client device comprising: a processing unit comprising one or more processors; an I/O subsystem configured to provide electronic learning content, and to receive user input data relating to the provided electronic learning content via one or more input devices connected to the client device; and memory coupled with and readable by the processing unit and storing therein a set of instructions which, when executed by the processing unit, causes the client device to: provide electronic learning content to one or more users via the I/O subsystem; receive user feedback data relating to the provided electronic learning content via the I/O subsystem; and transmit the user feedback data relating to the provided electronic learning content to a feedback analytics server of the electronic learning system; and a feedback analytics server of the electronic learning system, comprising: a processing unit comprising one or more processors; and memory coupled with and readable by the processing unit and storing therein a set of instructions which, when executed by the processing unit, causes the feedback analytics server of the electronic learning system to: receive a plurality of feedback data from the one or more client devices, the received feedback data corresponding to user feedback of one or more users relating to electronic learning content; determine an associated user and a sentiment score for each of the received plurality of feedback data; group the plurality of feedback data into one or more feedback aggregations associated with the one or more users; calculate a sentiment score for each of the one or more feedback aggregations associated with the one or more users using a language processing engine to determine a sentiment score for text feedback data relating to the electronic learning content; receive 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; store 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; receive 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; calculate 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, determine a user record prediction for the first user using the trained machine learning algorithm, based on the calculated sentiment score for the first user; determine a sentiment analyzer output for the electronic learning system and one or more output devices, based on the determined user record prediction for the first user; and provide the determined sentiment analyzer system-output for the electronic learning system to the determined one or more output devices.
1. A sentiment analyzer for an electronic learning system comprising: one or more client devices of the electronic learning system, each client device comprising: a processing unit comprising one or more processors; an I/O subsystem configured to provide electronic learning content, and to receive user input data relating to the provided electronic learning content via one or more input devices connected to the client device; and memory coupled with and readable by the processing unit and storing therein a set of instructions which, when executed by the processing unit, causes the client device to: provide electronic learning content to one or more users via the I/O subsystem; receive user feedback data relating to the provided electronic learning content via the I/O subsystem; and transmit the user feedback data relating to the provided electronic learning content to a feedback analytics server of the electronic learning system; and a feedback analytics server of the electronic learning system, comprising: a processing unit comprising one or more processors; and memory coupled with and readable by the processing unit and storing therein a set of instructions which, when executed by the processing unit, causes the feedback analytics server of the electronic learning system to: receive a plurality of feedback data from the one or more client devices, the received feedback data corresponding to user feedback of one or more users relating to electronic learning content; determine an associated user and a sentiment score for each of the received plurality of feedback data; group the plurality of feedback data into one or more feedback aggregations associated with the one or more users; calculate a sentiment score for each of the one or more feedback aggregations associated with the one or more users using a language processing engine to determine a sentiment score for text feedback data relating to the electronic learning content; receive 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; store 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; receive 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; calculate 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, determine a user record prediction for the first user using the trained machine learning algorithm, based on the calculated sentiment score for the first user; determine a sentiment analyzer output for the electronic learning system and one or more output devices, based on the determined user record prediction for the first user; and provide the determined sentiment analyzer system-output for the electronic learning system to the determined one or more output devices. 6. The sentiment analyzer system of claim 1 , the memory of the feedback analytics server storing therein further instructions which, when executed by the processing unit, cause the feedback analytics server to: group the plurality of feedback data into at least a first feedback aggregation and a second feedback aggregation, wherein the first and second feedback aggregations correspond to a same subset of the one or more users, and wherein the first feedback aggregation corresponds to a first feedback time period and the second feedback aggregation corresponds to a second feedback time period; calculate a sentiment score for the second feedback aggregation; and determine a change in sentiment score for the same feedback context between the first feedback time period and the second feedback time period, by comparing the sentiment score for the second feedback aggregation to the sentiment score for the second feedback aggregation, wherein the sentiment analyzer system output is determined based on the change in sentiment score.