Text analytics is more when harry met sally than predator vs alien v1


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Text analytics works best when it works hand-in-hand and digit-in-digit with structured data.
This slide is filleted from a presentation by the Medway Youth Trust on their highly successful text analytics project.

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  • Must state what we’re here to answer
  • Must state what we’re here to answer
  • I’d like to introduce you to two young people – Roxanne and Ben. There is nothing unusual or massively different about them to any other of the thousands of young people we work with each year but our Business Analytics project identified them as at risk of disengaging from learning and employment. And I would like to explain how.Behind each young person there is a story. Every single contact, intervention, action, correspondence, and support our charity’s staff and volunteers provide for a young person is recorded in our data base – it is a database which is compliant with UK Government recording requirements for aspects of our work. Our data represents a remarkable amount of intelligence about each young person. Historically we have approached our work through the analysis of our descriptive data; our numeric data. This is how local authorities and government tend to create analysis and make resource decisions. But we wanted to turn our unstructured data – our free text information – in to usable and analysable data too. We classify this as the interaction data – notes about our interventions, attitudinal data – notes about the young person’s opinions and hopes, and behavioural data. We wanted to know why one young person was in work or learning, and his next door neighbour was not. We wanted to understand the different inflexions in their life curves ... Why did one young person make one choice and the other something different. Bringing all of our data together provides us with what we call a 360 degree view of each young person.
  • Text analytics is more when harry met sally than predator vs alien v1

    1. 1. Why text analytics is more “When Harry Met Sally” Than “Predator Vs. Alien”
    2. 2. This is from a brilliant case study on Text Analytics by the Medway Youth Trust, authored by Gary Seaman.
    3. 3. UNSTRUCTURED DATA Interactions: Intervention recording Text Messages/Email Action Plans S139a Reviews UNSTRUCTURED DATA Attitudinal Opinions Preferences Needs Desires STRUCTURED DATA Age Gender Ethnicity Postcode Support Levels Vulnerable Groups UNSTRUCTURED DATA: Behavioural Attendance Number of Interventions Destination History Exam History Unstructured data – we had 175,000,000 words we were not using before this project