![]() Various issues can easily crop up with this approach. It probably took me less than 10 minutes to create this, and the result is so encouraging! But wait… Everyone loves simplicity. Here it is applied to a Net Promoter Score survey where column B contains open-ended answers to questions “Why did you give us this score”: 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. Here is my summary to break down these methods into 5 key approaches that are commonly used today. Happy to discuss this with anyone who is interested in providing feedback. I’ll try to be objective in my review, but of course, I’m biased because of my position. So, it’s fair to say, I’m qualified to speak on this topic. ![]() The highlight of my text analytics career was at Google, where I wrote an algorithm that can analyse text in languages I don’t speak.Īnd for the past 3 years, in my role as the CEO of Thematic I‘ve learned a lot about what’s available in the market. My academic research resulted in algorithms used by hundreds of organizations (I’m the author of KEA and Maui). I’ve spent the last 15 years in Natural Language Processing, specifically in the area of making sense of text using algorithms: researching, creating, applying and selling the technology behind it. Some try to reinvent the wheel by writing their own algorithms from scratch, others believe that Google and IBM APIs are the saviours, 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 who are living through the pain of analyzing text and trying to find a solution. ![]() Some Text Analytics background…įor a long time, I’ve been planning to write a post to clarify what’s possible in text analytics today, in 2018. In addition, with the help of text analytics software such as Thematic, companies can find recurrent and emerging themes, tracking trends and issues, and create visual reports for managers to track whether they are closing the loop with the end customer. Subsequently, we use text analytics to help companies find hidden customer insights and be able to easily answer questions about their existing customer data. To take Thematic as an example, we analyze the free-text feedback submitted in customer feedback forms, which was previously difficult to analyze, as companies spend time and resource struggling to do this manually. To make text analytics the most efficient, organisations can use text analytics software, leveraging machine learning and natural language processing algorithms to find meaning in enormous amounts of text. The idea is to be able to examine the customer feedback to inform the business on taking strategic action, in order to improve customer experience. For example, this can be analyzing text written by customers in a customer survey, with the focus on finding common themes and trends. Text analytics is the process of extracting meaning out of text. ![]() These methods range from simple techniques like word matching in Excel to neural networks trained on millions of data points. 5 Text Analytics Approaches: A Comprehensive Review Feedback AnalysisĪre you receiving more feedback than you could ever read, let alone summarize? Maybe you’ve used Text Analytics methods to analyze free-form textual feedback? ![]()
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