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 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.