Text analytics for MR: Methods, AI tools and the human touch

Learn which text analytics methods are available today and when & how (not) to use them

This course is now delivered online as a series of live sessions which fully meet the learning objectives of face-to-face training.

 Pulling the marketing curtains aside on AI powered text analytics:

In a first phase you will discover what kind of text analytics methods are available nowadays (sentiment analysis, entity extraction, categorization etc.) and where they can be applied in MR. The different techniques will be put to the test interactively by you and other delegates, followed by a group discussion.

The second part delivers some background on the different classes of AI software (Library, API, User facing), the effort required to implement them as well as methods for integrating them into existing software landscapes. We also discuss how to evaluate the quality of the offerings and how to compare solutions. A quick glimpse on the inner workings of AI software rounds of this block.

In the final part you will get the chance to dive into the analysis of a dataset of your choice (actual project of yours or a toy-dataset): Using our platform which combines AI with human supervision, you will experience how we see the future of professional AI products: A collaboration of software, responsible for the repetitive part, and humans, being required for making sense of it all.

Who will benefit?

Juniors researchers: Get to know tools that might make your life easier.

Senior researchers / directors: Better understand todays AI landscape and how to evaluate vendors (AKA when to call BS)

Learning method

Presentation, short individual tasks followed by group discussion

Course delivery mode

Online with breakout sessions of 3-4 persons.

Trainer biography

Maurice has an IT background, focused on Machine Learning techniques for language and image problems, where he published multiple scientific papers and won the prestigious SemEval competition. Combining this with hands-on entrepreneurial and consulting (Bain & Company) experience, he entered the world of market research in 2017 by co-founding Caplena - striving to make the recent achievements in machine learning accessible to the industry. As expert in the analysis of user-feedback, Maurice gives lectures within MAS courses at ZHAW and HWZ and has presented at various conferences, including IIEX Amsterdam, Quirks Chicago and VSMS Zurich.

Maurice holds a MSc ETH, Computational Science & Engineering.