Artificial intelligence driven system for managing deployment actions based on sentiment analysis

A system comprising:

• at least one computer processor;
• a deployment action handler executing on the at least one computer processor comprising:
• a vector encoding based feedback filter configured to perform first operations comprising:
• obtaining an assigned feature name for an application feature of a target application,
• selecting, using respective vector encodings of an application feature name, a set of application feature names comprising the assigned feature name, and
• filtering a collection of feedback using a set of application feature names of an application feature to obtain a filtered set of feedback,
• a prompt creator configured to perform second operations comprising:
• generating a sentiment request prompt requesting a sentiment score and comprising the filtered set of feedback and the set of application feature names, and
• a sentiment interface configured to perform third operations comprising selecting a recommended action based on the sentiment score, and updating the target application according to the recommended action;
• and
• a large language model (LLM) configured to execute on the at least one computer processor to perform fourth operations comprising processing the filtered set of feedback with the set of application feature names to generate the sentiment score for the application feature.

A non-transitory computer readable medium comprising computer readable program code for causing a computer system to perform operations comprising:

• obtaining an assigned feature name for an application feature of a target application;
• selecting, using respective vector encodings of an application feature name, a set of application feature names comprising the assigned feature name;
• filtering a collection of feedback using a set of application feature names of an application feature to obtain a filtered set of feedback;
• generating a sentiment request prompt requesting a sentiment score and comprising the filtered set of feedback and the set of application feature names;
• processing, by a large language model (LLM), the filtered set of feedback with the set of application feature names to generate the sentiment score for the application feature;
• selecting a recommended action based on the sentiment score;
• and
• updating the target application according to the recommended action.