AI and Machine Learning: Measuring Emotion

The Culturise Collaboration’s recent work demonstrates how new tech-based approaches can extract and quantify emotional content from unstructured data.

What you’ll learn:

• New and award-winning approaches, with case studies showing how to deal with qualitative research, at scale, and in ways that create new forms of data
• How commercial applications can be used – sometimes in real time – and the actions that result
• Where the notion of implicit embedded emotion might take us (albeit still at an early stage in terms of AI-based analysis)
• What the role of ‘normal’ qualitative research is

Description:

The Culturise Collaboration has spent the past year working on a series of projects that show how new tech-based approaches can extract and quantify emotional content from unstructured data.

Andy Dexter shares his insights, for instance looking at ongoing data collected in the retail banking sector, by MESH Experience, to examine metrics around cultural affinity as part of the ongoing Happiness Project.

· MESH collects thousands of open ended comments and pictures every month – a very rich dataset. How can these tools can be applied to commercial case studies?

· MESH’s methodology is continuous and real-time, revealing how language, topics of conversation and emotions change and evolve over time

Andy Dexter’s examples show how the language people use (both words and visual) can be correlated with structured data, such as positivity of experience, to create composite metrics that have until now been inaccessible.

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