Deciphering the meaning behind text can sometimes be really hard. Think of all the English and literature classes you took in high school where you would painstakingly review certain chunks of classic novels. You’d spend hours trying to decide the meaning behind why the author had the main character eating a pear instead of an apple. While this may seem like a silly example, it just goes to show how much stock people put into words and sections of text.
Well, companies actually do the same thing. Your business, research facility, law firm, or government organization can gain a ton of information about the people they’re servicing through text mining. Gain insights from the millions of words on the internet and the specific things people are saying about your organization. This unstructured data needs to be transformed into readable, organized data silos that can inform your company to make better business decisions. This is a data-science process known as text analytics. Let’s dive deeper into what text analytics is and how the different uses can make a difference for you and your organization.
What is text analytics?
Text analytics is a business intelligence technique that transforms unstructured text and data without predetermined formats into specific patterns so you can gain insights from this information. When you use text analytics software, you can make better decisions by harnessing the immense amount of data that is at your fingertips. So many industries benefit from this technology. Whether you’re a retail business looking to follow new trends, researcher looking into historic data, or government agency trying to figure out political optics, it will help to understand what people are saying online. Use all the tools at your disposal to make crucial decisions with text mining and analytics.
What are the steps of text analytics?
Text analytics involves text mining and a number of other steps. First, you have to gather the data. Text comes from everywhere including blog posts, customer reviews, emails, forum discussions — you name it. Once all that text is gathered in one place, you can begin to process it. Prepare the data using some of the techniques we’re going to talk about next. After that, you use text analytics to make predictions and test new models to please your customers. Classify and extract crucial information so you can form the best business plan for your organization based on reputable insights.
There are several examples and use cases of this software.
When you start using text analytics, there are several different use cases you’ll come across. After all, with so much text out there, you need different ways to analyze and organize it. One of the first use cases is sentiment analysis. This simple analysis mines text to find certain words, sections, or phrases that have emotional connections. You can see positive versus negative reviews or even dive deeper if you choose. Next, you can try topic modeling. This helps comb the internet for different sections of text and information that relate to the same topic to help researchers or trend monitors. Get even more specific with Name Entity Recognition (NER), as you can pinpoint certain people, places, or things. Search for nouns to see how a specific product is being mentioned or to follow stories in a particular area. Lastly, you can try event extraction, where you process information based on certain events that could affect your company. Some major examples of this would be a company merger or acquisition.
Overall, all these use cases work together to benefit your company by helping you see a holistic view of different trends. Find the information you need faster and more effectively with text analytics.
