Systems and methods for generating codes and code books using cosine proximity

A computer-program product for grouping customer comments, comprising:

• instructions stored on a memory and executable by a computer processor to cause the non-transitory computer-program product to:
• receive a text input comprising one or more customer reviews of consumer products and services;
• display a graphical user interface comprising the text input;
• accept a user command on the graphical user interface that causes the computer-program product to:
• partition the text input into one or more segments;
• generate one or more numerical vectors associated with each of the one or more segments;
• compare the one or more numerical vectors to generate, using a pre-trained machine learning model comprising a large-language model, a plurality of cosine proximity values associated with the one or more numerical vectors;
• apply a clustering algorithm to the one or more numerical vectors to generate one or more clusters of segments within one or more cosine proximity ranges, wherein each of the one or more clusters of segments corresponds with a sentiment;
• generate, using the pre-trained machine learning model, one or more groups of customer comments associated with each of the clusters of segments within the one or more cosine proximity ranges, wherein each group is associated with a category related to the one or more customer comments and customer reviews;
• and
• display the one or more groups of customer comments to a user on the graphical user interface;
• net each of the one or more codes into one or more super-categories by inputting the one or more codes into the large-language model, wherein the large language model provides the one or more super-categories as an output, and each super-category comprises at least one code of the one or more codes;
• and
• display the one or more super-categories on the graphical user interface, wherein each super-category is configured to be expanded to display corresponding at least one code.

A system for categorizing codes based on sentiment, comprising:

• a processor;
• memory coupled with the processor;
• instructions stored in the memory and executable by the processor to cause the system to:
• receive a text input comprising one or more customer comments and customer reviews of products and services;
• partition the text input into one or more segments;
• generate one or more numerical vectors associated with each of the one or more segments;
• compare the one or more numerical vectors to generate, using a pre-trained machine learning model comprising a large-language model, a plurality of cosine proximity values associated with the one or more numerical vectors;
• apply a clustering algorithm to the one or more numerical vectors to generate one or more clusters of segments within one or more cosine proximity ranges, wherein each of the one or more clusters of segments corresponds with a sentiment;
• generate, using the pre-trained machine learning model, one or more codes associated with each of the clusters of segments within the one or more cosine proximity ranges, wherein each code is a category related to one of the one or more customer comments and customer reviews;
• display the one or more codes to a user on a graphical user interface;
• net each of the one or more codes into one or more super-categories by inputting the one or more codes into the large-language model, wherein the large language model provides the one or more super-categories as an output, and each super-category comprises at least one code of the one or more codes;
• and
• display the one or more super-categories on the graphical user interface, wherein each super-category is configured to be expanded to display corresponding at least one code.