Archives by: Alyona Medelyan

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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Sarcasm in Customer Feedback – How common is it?

Most people believe that text analytics solutions fail because sarcasm in customer feedback is very common. Somebody writes “Great service, yeah right!” and the dumb algorithm tags it as positive. So, whenever I speak on text analytics, someone in the audience will always ask: But how do you deal with sarcasm?

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Emotional Analysis of Customer Feedback – The Missing Link

According to Bruce Temkin’s 2016 study, after a positive emotional experience, customers are 15 times more likely to recommend a company. 15 times more likely! That’s a huge difference. Not surprisingly, emotion analysis is receiving a lot of buzz. But do the current solutions deliver on the key question that companies should be asking themselves: How can we provide a positive emotional experience to our customers? How can we provide a positive emotional experience to our…

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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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Actionable insights: can data analysis software deliver them?

When it comes to making sense of data, getting actionable insights is the holy grail. But what does this even mean? When is a finding an insight? When is an insight actionable? Can data analysis deliver them? Let’s get to the bottom of this by looking at some examples. Imagine, you have conducted a survey of 100,000 students, and you seek actionable insights for what to improve at a university. Non-insightful vs. Insightful Knowledge Non-insightful…

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Customer Experience Update