Four Things to Know About Text Mining

It should be no surprise that big data and artificial intelligence are here to stay, and that means big changes across industries.

It’s easy to get bogged down by these concepts, since they’re often presented in ways that either seem gimmicky or intellectually intimidating. So when facing the pressure of being an early adopter in the fast-paced world of technology, it’s crucial to understand as much as you can before jumping in.

Data recovery and analysis is an especially touchy subject in this time of updated privacy policies and data ethics. One particular branch of data mining is text mining, which focuses on the discovery and analysis of text-based data. This data often comes in unstructured formats, such as social feeds, survey responses, or comments on a webpage. Beyond the necessity for ethical data recovery, there are a few things to know about text mining that give it some added value in the digital world. We’ll outline four main points to help ease you into text mining.


Text mining goes deeper than A Simple search

Data analysis is easier with processes like text mining, freeing up your time to apply your new insights to business decisions.

What many don’t understand about data mining in general is that it isn’t just about extracting data. When faced with a mountain of text that you don’t want to read through to find a particular topic, click Control+F (or Command+F, if you’re so inclined) for a simple document search and you’ll find just what you’re looking for. But what if you don’t know exactly what you are looking for? 

Text mining does not just identify specific topics within documents. It uses intelligent algorithms that can pinpoint topics in relation to other sections of text. The really useful part about this is that you can discover new relevant data to go with your preexisting topics. So, the algorithm doesn’t just spit out topics that you identify, but it also identifies new information based on the relationships between text segments within the document. So if you want to analyze for text relative to “ice cream” it might come up with anything from favorite flavors to vegan alternatives, and even more that you might never think to associate. You can then categorize, analyze, and draw specific insights from this data with ease.


Text mining has a multitude of use cases

Data analytics give you the power to draw associations between unexpected topics. What kind of circumstances would call for this sort of data association? Well, think about your brand. How do your consumers really see you? Boolean searches on social media data will give you details on topics you’re already aware of, but what else are consumers talking about? Text mining can identify relationships between your brand and unexpected strings of text. These strings will help to identify topics that you had not heard customers mention yet. They’re not obvious, but for that reason they’re often overlooked when considering a brand image.

This applies to a number of areas and industries. Doctors might use text mining software to analyze medical documents and draw associations between symptoms to identify diseases. Email services use text mining to identify spam emails based on wording, just as search engines use its concepts to rank websites by relevance to your search terms.


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What kind of data is used?

This is not personal private data that is being analyzed. The text comes from documents such as product listings, blog comment sections, forums, and social media profiles. The data was made public by the posters because they actually want their opinions heard on a topic. The only trouble is, there are so many posts across numerous platforms, and it’s hard to keep up. That’s why it takes mining algorithms to extract the information.

To ignore this data would be to ignore vital feedback for various topics, whether related to travel destinations, celebrity gossip, or academic forums. From public relations to brand management to product development, the data gleaned from these posts provide valuable insights into the minds of consumers.


AI Algorithms make Data analysis is more efficient

You can take all the time in the world to manually scroll through online platforms and identify your brand among thousands of others. The fact remains that a computer can do the same thing faster and with better accuracy. What it would normally take a person several months or years to complete, an AI powered program could do in a matter of days or weeks. On top of that, programs can do this accurately with the help of NLP techniques, which helps to make sense of ambiguous and unstructured text data (e.g., words with multiple spellings, varied grammar structures). So even when an algorithm is searching for mentions of "good" restaurants, they will still know to include ones that are "not bad." 

With the time you would have spent reading documents freed up, you can spend more energy on using the resulting data to better your business activities. What do these data associations mean to your industry, brand, or products? Having the data gives you the power to make changes to your strategy to best match your competitors and the market as a whole.


There's a lot of pressure to join the AI and data analytics game, but it's helpful to be an informed user. Understanding the basics of text mining and similar topics will help you identify practical uses for your business goals, rather than jumping in to a world completely unknown to you.