I wanted to share with you my learnings from when our team at Thematic organized and sponsored a free 3-hour workshop on Deep Learning for Sentiment Analysis, run by Dr. Felipe Bravo-Marquez. The workshop booked out within 24hrs of announcing it and more than 40 people attended.
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Have you heard of Thematic analysis? If you are working in insights or data analysis, you will have heard of Sentiment Analysis. It answers the question “Is this mentioned in a positive or a negative light?”.
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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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Have you wondered what customer sentiment analysis really is? Let’s get to the bottom of it. But first, let’s set some context. Customers are looking for positive experiences. Brands do too!
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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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Understanding customer comments, on a large scale, needs to be automated. So, it requires Natural Language Processing (NLP) or Text Analytics. Unfortunately, most open-source NLP tools were developed on text researchers have easy access to. These are typically news articles, research papers and movie reviews. I learned that the analysis of customer comments is quite different, and here is why.
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In July 2016, I was fortunate enough to speak at the Sentiment Analysis Symposium in New York. It is one of the most important events for those who invent text analytics solutions and for those who use them. I also attended the co-located sentiment analysis tutorial run by Jason Baldridge. Overall, this was an excellent event to get up to speed on the current state of affairs and the future outlook. So, here are my…
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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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Maya Angelou once said “people will forget what you said, people will forget what you did, but people will never forget how you made them feel.” Results from a recent Mckinsey study demonstrate what this means for businesses: After a positive customer experience, more than 85 percent of customers purchased more. After a negative experience, more than 70 percent purchased less. So getting this wrong can prove a costly exercise. Rather than rely on assumption,…
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