Textual analytics is at an estimated market value of $3 billion and is only expected to grow, possibly even double in the next two years. But what exactly is this industry and why has it become so popular?
Text analytics software takes structured data and determines algorithms or patterns to figure out information such as customer opinions and product reviews in order to make better decisions based on the facts at hand. This kind of software is usually used by customer service oriented corporations, but it can also be used by government agencies to look at unique patterns of similar identities, dates, places, people, and other information that may seem random but can hold deeper information.
This analytic software can also look for information on emotions through sentiment analysis. This type of information in particular is where an emotion AI can come in handy. That is to say, a machine that can understand and process people’s emotions.
What makes an emotion AI so necessary within this kind of industry? Well, machines can process more information than we can as people. As it stands right now, the International Data Corporation estimates that only 1% of data is analyzed at all. The IDC currently estimates that every person creates approximately 2 MB of new information every second of every day. Text mining can make use of this information which is included in that 99% we currently aren’t using for anything. For example, social media analysis could be a great help to us. Facebook alone has almost 2 billion active users all over the world, and that provides us with an extraordinary amount of information that could give businesses all sorts of information that they may not be aware of otherwise.
Text mining’s main goal is to help businesses learn more about their customers and what they would like to see. It provides this information through 3 main functions: providing more accurate insights of the customer base over a broader range of sources, detecting possible risks, compliances, and detections of threats that the business may not have been aware of, and improving customer engagement and relations by predicting what customers might be thinking. This makes it an invaluable tool not just for data analysists, but businesses and corporations as well.