Abstract: A method and system for generating clusters for feedback data may include receiving a request for clustering feedback data, the request including one or more parameters relating to the feedback data, retrieving a plurality of vectorized feedbacks stored in a key value format, the key value format including a feedback identifier used as a key for each of the plurality of vectorized feedbacks, creating a plurality of feedback clusters based on the one or more parameters and the retrieved plurality of vectorized feedbacks, the plurality of feedback clusters categorizing at least some of the feedback data into the plurality of feedback clusters, and transmitting data relating to the plurality of feedback clusters for display on a user interface screen.

Abstract: A system and method for creating a machine learning (ML) classifier for a database uses a weakly-supervised training data set created automatically from database items on the basis of a human-created keyword set. The automatically created training data set is used to construct one or more deep learning classifier checkpoints, which can then be compared with one another and with a classifier based on the original keyword set in order to select a classifier for use by other users viewing the database.

Abstract: A system and method for creating a machine learning (ML) classifier for a database uses a weakly-supervised training data set created automatically from database items on the basis of a human-created keyword set. The automatically created training data set is used to construct one or more deep learning classifier checkpoints, which can then be compared with one another and with a classifier based on the original keyword set in order to select a classifier for use by other users viewing the database.

Abstract: A method and system for identifying statistically significant changes in user ratings includes receiving a request to identify a statistically significant change in a parameter associated with changes in user ratings between a first time period and a second time period, retrieving data from a first data structure associated with the user ratings during the first time period and a second data structure associated with the user ratings during the second time period, each of the first and the second data structures including a plurality of dimensions for each user rating and each dimension having one or more levels, identifying, based on the retrieved data, the statistically significant change in the parameter between the first time period and the second time period, identifying a dimension and a level associated with the identified statistically significant change, and providing display data for generating a user interface (UI) screen to display information about the identified statistically significant change.

Abstract: A method and system for displaying changes in user ratings may include receiving a request to calculate a value for an amount of change in a parameter associated with user ratings between a first time period and a second time period, retrieving data from a data structure associated with the user ratings, the data structure including a plurality of dimensions for each user rating and each dimension having one or more levels, calculating based on the retrieved data the value for the amount of change for at least one of the plurality of dimensions, quantifying, for the calculated value, a first portion resulting from changes in user population and a second portion resulting from true change in the parameter associated with user ratings, and providing display data for generating a user interface (UI) screen to display at least one of the calculated value, the first portion and the second portion on a display device.