Complaint classification in customer communications using machine learning models

Complaint classification in customer communications using machine learning models

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 ind...

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Bibliographische Detailangaben
Hauptverfasser: Parrish, Nathan R, Volin, Noel P, Mann, Thomas, Portis, Kevin, Ranganathan, Raja, Soistman, Michael W, Arnesen, Dane, Bocketti, Shawn, Engebretson, Josh
Format: Patent
Sprache:eng
Schlagworte:
ACOUSTICS
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
ELECTRIC DIGITAL DATA PROCESSING
MUSICAL INSTRUMENTS
PHYSICS
SPEECH ANALYSIS OR SYNTHESIS
SPEECH OR AUDIO CODING OR DECODING
SPEECH OR VOICE PROCESSING
SPEECH RECOGNITION
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung: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.