Luckily, there are AI text analytics solutions that can find themes in your text automatically! Text mining in excel can be time consuming, and if you have large amounts of data it can quickly become difficult to handle. Part 2 is live! Manual rules, another approach to text analytics. That’s it for now, watch out for Part 2, coming soon! I’ll also talk about what actually does work and is a good approach. There are also many other disadvantages to DIY word spotting, that we’ll discuss in the next post. If you need to maintain the data consistently over time.If you need to share the results with your colleagues.If you need to visualize the results (Excel will hear you swearing).If you won’t have time to review and correct the accuracy of each piece of text. If you have any substantial amount of data, more than several hundred responses.When word spotting failsĪs for the downside? Please don’t use word spotting: If the dataset is small, you can review the results and ensure high accuracy very quickly. If you have a dataset with a couple of hundred responses that you only need to analyze once or twice, you can use this approach. And indeed, I’ve talked to companies who hand-crafted massive custom spreadsheets and are very happy with the results. You can imagine that the formula above can be tweaked further. Would you bet your customer insights on something that’s at best 50 accurate? When word spotting is OK “Billing” is actually about “Price”, and three other comments missed additional themes. Out of 7 comments, here only 3 were categorized correctly. You can type in a formula, like this one, in Excel to categorize comments into “Billing”, “Pricing” and “Ease of use”: How to build a Text Analytics solution in 10 minutes Or, you could write a script in Python or R. You can implement word spotting in an Excel spreadsheet in less than 10 minutes. The beauty of the word spotting approach is its simplicity. For example, if words like “price” or “cost” are mentioned in a review, this means that this review is about “Price”. The main idea behind text word spotting is this: If a word appears in text, we can assume that this piece of text is “about” that particular word. It’s loved by DIY analysts and Excel wizards and is a popular approach among many customer insights professionals. There is also keyword spotting, which focuses on speech processing.īut to my knowledge, word spotting is not a used for any type of text analysis.īut I’ve heard frequently enough about it in meetings to include in this review. In fact, in the academic world, word spotting refers to handwriting recognition (spotting which word a person, a doctor perhaps, has written). The academic Natural Language Processing community does not register such an approach, and rightly so. Introducing word spotting: How to DIY in Excel or Python So, I decided to post a series of articles that dig deeper into the 5 most common Text Analytics approaches and examines their pros and cons. Some try to reinvent the wheel by writing their own algorithms from scratch, others believe that Google and IBM APIs are the savior, others again are stuck with technologies from the late 90’s that vendors pitch as “advanced Text Analytics”. Throughout my career, I’ve spoken with many people who are living through the pain of analyzing text and trying to find a solution. Part 1: How to build a text analytics solution in under 10 minutesįor a long time, I’ve been planning to write a post to clarify what’s possible in Text Analytics space today, in 2018.
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