Posts Tagged: Text analytics

Part 4: Topic Modelling, an approach to Text Analytics

This is the 4th article in my series of Text Analytics posts explaining popular approaches to feedback analysis. Last week, we talked about text categorization, a Machine Learning approach that requires training data. We concluded that it can’t detect emerging themes in people’s feedback and that it’s only as accurate as the supplied training data. Today, we’ll discuss topic modelling, also a Machine Learning approach, but an unsupervised one, which means that this approach learns…

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3 best practices for coding open-ended questions

Open-ended survey questions often provide the most useful insights, but if you are dealing with hundreds or thousands of answers, summarising them will give you the biggest headache. The answer lies in coding open-ended questions. This means assigning one or more categories (also called codes) to each response. But how to go about it?

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Why word clouds harm insights

The picture above depicts Paul McCartney wearing a mullet. This article criticizes word clouds, the mullets of the Internet. :-) “Every time I see a word cloud presented as insight, I die a little inside” J. Harris, data journalist   If you are a manager, there is a high chance that you’ve encountered word clouds in reports on key company issues, such as customer service or employee satisfaction. I still remember the first time I encountered…

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Why most customer feedback analysis tools suck and how to fix this

They collect scores into pretty dashboards, but don’t actually tell what the feedback is or how to achieve customer loyalty. Customer feedback analysis tools are all the rage, but most of them suck. If you ever left a review yourself, you will know that your score is not nearly as valuable as the review itself. Think about it: as a business, how useful is the average of 100 scores compared to 10 customer comments on…

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Customer Experience Update