Techniques for aggregating insights of textual data using hierarchical clustering

A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:

• applying a trained clustering model to a plurality of textual data, wherein the trained clustering model determines at least one cluster of textual data based on a meaning of the textual data, wherein textual data of the at least one cluster is a portion of the plurality of textual data;
• generating a name, using a trained naming model, for each of the at least one cluster based on textual units in the respective at least one cluster, wherein the generated name indicates a theme that represents the meaning of the textual data of the at least one cluster;
• analyzing the at least one cluster to determine a distribution metric of the at least one cluster;
• and
• generating a notification, with respect to at least one theme, based on a plurality of rules applied to the determined distribution metric and the at least one cluster associated with the at least one theme.

A system for discovering and aggregating themes, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• apply a trained clustering model to a plurality of textual data, wherein the trained clustering model determines at least one cluster of textual data based on a meaning of the textual data, wherein textual data of the at least one cluster is a portion of the plurality of textual data;
• generate a name, using a trained naming model, for each of the at least one cluster based on textual units in the respective at least one cluster, wherein the generated name indicates a theme that represents the meaning of the textual data of the at least one cluster;
• analyze the at least one cluster to determine a distribution metric of the at least one cluster;
• and
• generate a notification, with respect to at least one theme, based on a plurality of rules applied to the determined distribution metric and the at least one cluster associated with the at least one theme.