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US 9,336,268 B1Granted
Relativistic sentiment analyzer
AssigneePearson Education, Inc.
InventorsChristopher Moudy, Todd Paterson, Kevin Berns
Life of this patent
Estimating the term…
Abstract
Sentiment analyzer systems may include feedback analytics servers configured to receive and analyze feedback data from various client devices. Feedback data may be received and analyzed to determine feedback context and sentiment scores. In some embodiments, natural language processing neural networks may be used to determine sentiment scores for the feedback data. Feedback data also may be grouped into feedback aggregations based on context, and sentiment scores may be calculated for each feedback aggregation. Sentiment analyzer outputs and corresponding output devices may be determined based on the sentiment scores and feedback contexts.
Claims20
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.
23 more elements
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 more elements
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.
10 more elements
Dependencies are inferred from the claim text.