Abstract: A method including preprocessing natural language text by cleaning and vectorizing the natural language text. A first machine learning model (MLM) extracts negative reviews. A first input to the first MLM is the natural language text and a first output of the first MLM is first probabilities that the negative reviews have negative sentiments. The method also includes categorizing the negative reviews by executing a second MLM. A second input to the second MLM is the negative reviews. A second output of the second MLM is second probabilities that the negative reviews are assigned to categories. The method also includes identifying, using a name recognition controller and based on categorizing, a name of a software application in the negative reviews and sorting the negative reviews into a subset of negative reviews relating to the name. The software application is adjusted based on the subset of negative reviews.

Abstract: A method including receiving natural language text. A negative review is extracted from the natural language text using a first machine learning model (MLM). A first input to the first MLM is the natural language text and a first output of the first MLM is a first probability that the negative review has a negative sentiment. The negative review includes an instance of the natural language text having a corresponding negative sentiment probability above a threshold value. The negative review is categorized by executing a second MLM. A second input to the second MLM is the negative review. A second output of the second MLM is a second probability that the negative review is assigned to a category. A name of a target of the negative review is identified using the name recognition controller and the negative review. The name of the target and the category are provided.

Abstract: A method and system determines whether or not a new data acquisition process is working for individual financial accounts of users of a data management system. The method and system trains an analysis model with a machine learning process. The trained analysis model then analyzes financial data obtained by both an old data acquisition process and the new data acquisition process. The trained analysis model identifies whether the new data acquisition process is working properly based on the analysis.

Abstract: The present disclosure relates to processing domain-specific natural language commands. An example method generally includes receiving a natural language command. A command processor compares the received natural language command to a corpus of known commands to identify a probable matching command to the received natural language command, wherein the matching is based on an edition error and a recognition error. The corpus of known commands comprises a plurality of domain-specific commands, each of which is mapped to one or more domain-specific entities. Based on the comparison, the command processor identifies one or more entities in the received natural language command to perform an action on based on the mapping of the one or more domain-specific entities in the probable matching command and executes a domain-specific action included in the natural language command on the identified entity.

Abstract: The present disclosure relates to processing domain-specific natural language commands. An example method generally includes receiving a natural language command. A command processor compares the received natural language command to a corpus of known commands to identify a probable matching command in the corpus of known commands to the received natural language command. The corpus of known commands comprises a plurality of domain-specific commands, each of which is mapped to a domain-specific action. Based on the comparison, the command processor identifies the domain-specific action associated with the probable matching command to perform in response to the received command and executes the identified domain-specific action.

Abstract: The present disclosure relates to processing domain-specific natural language commands. An example method generally includes receiving a natural language command. A command processor compares the received natural language command to a corpus of known commands to identify a probable matching command to the received natural language command. The corpus of known commands comprises a plurality of domain-specific commands, each of which is mapped to one or more domain-specific entities. Based on the comparison, the command processor identifies one or more entities in the received natural language command to perform an action on based on the mapping of the one or more domain-specific entities in the probable matching command and executes a domain-specific action included in the natural language command on the identified entity.

Abstract: The present disclosure relates to processing domain-specific natural language commands. An example method generally includes receiving a natural language command. A command processor compares the received natural language command to a corpus of known commands to identify a probable matching command to the received natural language command. The corpus of known commands comprises a plurality of domain-specific commands, each of which is mapped to one or more domain-specific entities. Based on the comparison, the command processor identifies one or more entities in the received natural language command to perform an action on based on the mapping of the one or more domain-specific entities in the probable matching command and executes a domain-specific action included in the natural language command on the identified entity.

Abstract: The present disclosure relates to processing domain-specific natural language commands. An example method generally includes receiving a natural language command. A command processor compares the received natural language command to a corpus of known commands to identify a probable matching command in the corpus of known commands to the received natural language command. The corpus of known commands comprises a plurality of domain-specific commands, each of which is mapped to a domain-specific action. Based on the comparison, the command processor identifies the domain-specific action associated with the probable matching command to perform in response to the received command and executes the identified domain-specific action.