Abstract: Learning frameworks for processing text transcripts may include receiving, by an application, a query comprising a topic. A topic model may determine a plurality of subtopics based on the topic. The application may receive, from a database based on the topic and plurality of subtopics, a plurality of text transcripts. A sentiment model may compute, for each text transcript, a respective sentiment score based on a text of the respective text transcript. The application may determine, for each text transcript, a duration of a communication session associated with the respective text transcript. The application may compute, for each text transcript, a total score based on the sentiment score and the duration of the respective text transcript. The application may return, as responsive to the query, a subset of the plurality of text transcripts having a total score that exceeds a threshold.

Abstract: Systems, methods, and computer program products for identifying zones of interest in text transcripts. An application may receive input specifying a text statement type and determine a plurality of heuristics for identifying statements of the statement type in transcripts. The application may determine, based on a first heuristic, a first text statement of the statement type. The application may generate, based on a clustering algorithm, a plurality of additional statements of the statement type. The application may receive a first text transcript. The application may identify, based on a second heuristic, a first text statement in the first text transcript, where the first text transcript statement is of the statement type. The application may generate a graphical indication that the first text transcript statement is of the statement type, and display the first transcript statement and the graphical indication on a display.