Text Analytics: How To Analyse And Mine Words And Natural Language In Businesses
Most businesses have a huge amount of text-based data, such as memos, company documents, emails, reports, media releases, customer records and communication, websites, blogs and social media posts. Until recently it wasn’t always that useful, at least in terms of easily extracting business-critical insights. But that has all changed thanks to text analytics.
Text analytics, also known as text mining, is a process of extracting value from large quantities of unstructured text data. While the text itself is structured to make sense to a human being (i.e. A company report split into sensible sections) it is unstructured from an analytics perspective because it doesn’t fit neatly into a relational database or rows and columns of a spreadsheet. Traditionally, the only structured part of text was the name of the document, the date it was created and who created it.
Access to huge text data sets and improved technical capability means text can be analysed to extract high-quality information above and beyond what the document actually says. For example, text can be assessed for commercially relevant patterns such as an increase or decrease in positive feedback from customers, or new insights that could lead to product tweaks, etc. As such, text analytics is now capable of telling us things we didn’t already know and, perhaps more importantly, had no way of knowing before. And these insights can be incredibly useful in business.
Text analytics is particularly useful for information retrieval, pattern recognition, tagging and annotation, information extraction, sentiment assessment and predictive analytics. It could, for example, shed light on what your customers think of your product or service, or highlight the most common issues that your customers complain about.
Making sure your text is analysis-ready
It’s not enough for the text to be in a digital format, it also needs to be datafied. If you copied a page from a book as a jpeg file, you would technically have a digital copy of the text but it would be no good for running text analytics. What you need is datafied text like the text we see in many e-readers which allow you to interact with the text (by highlighting sections, adding notes, searching the text, etc.). So, any old paper files that you want to analyse will need to be rendered in a digital but also datafied format.
Once the text is ready there are a number of commercially available text analytic tools that can help you. Which one you use will depend on your objective.
