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Deep context

Deep context is a combination of quantitative machine learning and qualitative analytical processes that include a wide-spectrum of datasets, network analysis, sentiment and empathy based analysis . Added to these we factor the event proximity to populations in need, the utilisation of local knowledge and the understanding of transnational threats and regional dynamics, which is especially vital in mitigating against the risk 

See deep-context.com

customer experience. Context is a make or break proposition to any customer experience program. insights that are specific to your domain are essential to providing input and driving decision-making and strategy execution throughout an organization:

  • Gain insight into intent themes that provide insight into the exact aspects of the conversation you want to understand
  • Gain insight into how your brand perception across industry specific KPIs.
  • Context for each industry . Industries include retail, telecommunications, healthy living, fashion, restaurant, automotive, finance and more
  • Grow your business by understanding the loyalty concerns your target customers have, and how to address those concerns for customer retention
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Compare Algorithm Outputs to Reveal Underlying DataSet Structure

Problem: Data has noise. Data sets must be cleansed, normalized and curated. But you never really can tell for sure which algorithms to apply unless you compare results.

Steps:

  1. Plan to generate different visualizations  of your data set,
  2. Plan to create and various transforms on your data set
  3. Run through a number of typical algorithms on each view of your dataset.

Purpose:  Clarify which data transformations will reveal the structure of the dataset that underlies your problem.

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OCR text classification

One of the main decisions we have to make upfront is once we apply OCR to incoming unstructured data how do we classify it according to ontologies in that specific business, domain or even industry.

Domain ontologies serve as metadata or mid ontologies where we can consolidate unstructured and structured data that may have very little overlapping or mapping ontological schemas but can be used to consolidate and join them for subsequent congregation aggregation and data lake consolidation efforts.

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Pattern one:Routed ingestion

In this pattern we ingest content whether from an email or transcription of voice to text and then we classify then we perform tone and sentiment analysis will use natural language understanding to accomplish the above three then we apply a set of heuristics possible if then rules or machine learning based classification rules and dialogs in an intense based architecture and initiate a workflow which may be a routing to the appropriate Houman in the loop