Abstract
Systems and methods are described to address potential challenges in enterprise service management that can arise from delayed feedback and manual case routing. A described technology can utilize a processing pipeline to perform near real-time analysis of service interactions. This pipeline can ingest text-based communication data, sanitize it to remove personal information, and apply a computational linguistic model to classify user sentiment and identify predefined sensitive topics. Based on this analysis, a system may enable automated workflows, for example, the intelligent routing or escalation of cases that meet certain sentiment or sensitivity criteria. The system can also provide real-time sentiment indicators to service agents and aggregated data dashboards for management, which can supplement traditional feedback mechanisms and support more proactive service monitoring.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Singhal, Parnika and Raithatha, Deep, "Automated Service Case Triage Using Real-Time Sentiment and Sensitivity Analysis", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10689