Abstract: Techniques are described for performing team member behavior identification and classification using a machine learning model and one or more rule-based models for customer communications. A computing system receives a message from a user device. The computing system uses output of a machine learning model to determine whether the message includes an indication of team member behavior including at least one behavior term and at least one team member reference. The computing system also uses output of one or more rule-based models to determine whether the message includes an indication of a type of team member behavior including a type of behavior term and a type of team member reference substantially proximate to each other within the message. Based on the message including the indication of team member behavior, the computing system sends the message to another system corresponding to the type of team member behavior included in the message.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for generating an insight report. An example method includes receiving a configured input data set and selecting an insight engine configuration based on a configuration parameter set. The method further includes generating a n-gram term set and performing a streamline n-gram routine on the n-gram term set. The method further includes generating an emerging topic set for an interest population and generating a per-topic metric set for each topic identifier included in the emerging topic set. The method further includes generating an insight report which comprises each per-topic metric set and providing the insight report.

Abstract: An example computing system for classifying messages is described. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to receive data indicative of a message from a user device, wherein the data indicative of the message comprises a string of characters, identify, based on the string of characters, a set of token vectors from a plurality of token vectors generated based on a set of training data, and determine, using a machine learning model and based on the set of token vectors, a probability that a risk level associated with the message is a first risk level indicating that the message is to be elevated, wherein the first risk level is greater than a second risk level.