Abstract: A system and method for online user feedback management are provided. The method includes receiving online user feedbacks for a product from a plurality of users. A plurality of topics for the product are identified from the online user feedbacks. For each topic, the received online user feedbacks are categorized into a plurality of groups based on a rating score provided in each online user feedback for the product and semantic analysis of each online user feedback for the product. A net promoter score (NPS) uplift for each topic is calculated, where the NPS uplift measures an improvement in a NPS for the product if issues related to the topic are resolved. A priority topic is identified based on the NPS uplift for each of the topics. The priority topic is then prioritized in resolving issues related to the topics included in the online user feedbacks.

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 system and method for online user feedback management are provided. The method includes receiving online user feedbacks for a product from a plurality of users. A plurality of topics for the product are identified from the online user feedbacks. For each topic, the received online user feedbacks are categorized into a plurality of groups based on a rating score provided in each online user feedback for the product and semantic analysis of each online user feedback for the product. A net promoter score (NPS) uplift for each topic is calculated, where the NPS uplift measures an improvement in a NPS for the product if issues related to the topic are resolved. A priority topic is identified based on the NPS uplift for each of the topics. The priority topic is then prioritized in resolving issues related to the topics included in the online user feedbacks.

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.

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.