Identifying actionable insights in unstructured datatypes of a semantic knowledge database

Abstract

An artificial intelligence (AI) system for use by a business to process multiple channels of user feedback data. User experience feedback data is provided via multiple channels—including structured feedback in the form of surveys, and unstructured and unsolicited feedback provided by people who wish to provide ad hoc feedback. The unstructured feedback may be from social media posts, calls to a service center, emails, and other sources. The feedback is aggregated as text data in a data pool. A natural language processing machine learning system is used to analyze the feedback and extract the meaning in human-understandable terms. Clustering techniques are used to identify commonalities in the feedback data even when issues are found in different data channels using different terminology. The commonalities are analyzed to identify actionable insights which address the underlying issues. Labeled sample data is used to perform supervised learning of the AI system.