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About Multi-Channel Text Analytics

Multi-Channel Text Analytics leverages the Coveo Index to provide insight across vast amounts of data concurrently in disparate enterprise systems and social channels by enabling users to discover information relationships across diverse sources of data, behind the firewall, and in the social arena.

The Text Analytics extracts themes and named entities from the indexed documents and stores this metadata in the index. Themes are noun phrases extracted from full text based on linguistic analysis. A noun phrase is a sequence that can be replaced by a noun or pronoun. Named Entities are categories of information like people, companies, products, or places. Themes and named entities together with other metadata feed facets and widgets that present consolidated information.

Example: In a high technology enterprise, you can use Multi-Channel Text Analytics to find and correlate knowledge base and related contents as in the following role-based insight console for assisted service.

Facet showing tags entered by end-users
Facet showing themes from the result set
Facet showing product named entities from the result set
Mini-results showing recommended documents based on overall context
Controls to dynamically tag and link knowledge document
Widget showing recommended experts correlated using text analytics from result set and all enterprise content
Widget showing resolved tickets with similar symptoms
Widget showing related community content recommended through text analytics
Widget showing related social content recommended through text analytics

On the back-end side, the text analytics process collects the necessary metadata in the documents. The Coveo text analytics package contains preset filter, extractor, or normalizer pipeline stages that you can assemble to create a text analytics pipeline. Developers can also use the Text Analytics framework to build custom pipeline stages.

Note: Contact the Coveo Professional Services for more information and assistance for the implementation of Multi-Channel Text Analytics.

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