Natural Language Processing and Text Analytics had become a hit even before they were named, discussed, and studied. Only recently, artificial intelligence took over these fields and provided a much-needed boost. With the rise of big data, companies have been bombarded with more and more organizational data, mostly in the form of text. This has led to chaos and is causing much of the data to go unused and underutilized. The best way to handle this data stream is text mining or textual analytics. Text analytics is mainly the processing of a gigantic collection of textual information in order to find connections that are not possible for a human to draw. The existing information and data are transformed into new information and in this process, unstructured, unusable data is converted into structured data.
If we just look at the web data including social media, it’d be visible that the alt-data landscape provides us with one of the most unstructured data compared to any other sources. It gives us videos, images, hashtags, text (reviews, comments, posts, etc.), and more. The data is valuable to enterprises, governments, advertisement giants, financial services, media houses, defense agencies and scientific researchers. Since it can a perpetual source of market and customer data, companies are applying predictive tools on text data so as to grow their brands.
“Prevention is better than Cure”
So what if you could prevent crimes by knowing beforehand, where and when they might take place. Seems like a scene from a science fiction movie like Minority Report? Well, it is a reality now. Since the internet is anonymous and so are most of the communication software that operate via it, most criminals plan and communicate using these methods. However, you can understand that millions of normal people use these means of communications as well, and it is a difficult task to pinpoint messages that might be considered a threat. This is easily done using advanced text analysis software that scan communication sources in real time and sound different levels of threat alert on finding different types of text. Law enforcement across the world have been using these technologies so as to prevent terrorist attacks, catch sleeper cells and stop people from carrying out other unlawful activities.
Every industry wants to make itself aware of the risks that it is facing, or those that it might face in the near future. Because of this reason, risk analysts are in high demand in the recent years. Many in the finance industry, such as banks, micro-finance institutions, and others, are now depending on risk management software that can go through documents and profiles so as to decide on things like which firm to invest in, which people to give loans to, and more. The text mining technologies used by such high-end software absorb petabytes of data and present information in a consumable format. This helps in risk mitigation. Such software is helping financial institutions all over the world, to decrease their percentage of non-performing assets.
In many industries like the healthcare industry, managing a huge amount of textual information has become a problem. If you started building racks and kept all the documents related to healthcare on a single rack, scalable vertically, it would probably reach the moon. The amount of information gathered every single hour, is huge. All this data has to be stored in such a manner that the information can be retrieved as and when required. It may so happen, that there is an epidemic and hospitals need to coordinate to go through all their data to pinpoint the source or the first infected person. Such a huge exercise would be impossible without the help of proper text analytics systems in place that would manage the data and information and keep them in structured tree-like format. This would lead to people being able to access the data in any way they need- region-based, gender-based, disease-based, and more. Inability to find important information quickly may cripple such organizations dealing with large volumes of text documents.
Where is customer service heading? I believe this video – Google Assistant calls Restaurant can provide you the answer. Text mining and natural language processing are frequently being used in customer care services, be it over chat or voice call. The “press one for recharge, press two for …..” format has been changed to “say yes for account closure or no for cancellation …..” format in many places to make the system appear more humane. Most banks and e-commerce companies are using natural language processing based chat bots that try to mimic a human customer care officer when talking to a customer. Improvement in customer care experience is taking place as these bots are using information on the customer that they are interacting with, to make the experience more customized. They are using different sources of information that they collected from the user earlier, such as surveys, complaint tickets, and previous customer call notes, so as to improve the quality and effectiveness along with the speed in resolving problems. By automating customer care services, companies are providing customers a better experience while at the same time, saving money.
With rising cases of insurance fraud, text analytics has proved effective in going over huge collections of case files to understand the chances of an insurance claim being fraud. It greatly reduces the workload of the company officials since the fraud recognition software would automatically flag cases where a high probability of fraud is determined. Although the software is not fool-proof, it does act as a filter so that human attention can be directed only to cases that need it. Insurance companies are tying up with technology giants so as to take full advantage of the strides in text mining technologies, and combine their results to produce structured data to prevent frauds and swiftly process claims.
Remember how you saw ads of exactly the same mobile phone on Facebook that you were viewing in Amazon? No that is not a coincidence. Digital advertising has been revolutionized by text and web mining. Text data related to all that you type, view, or do online is stored by technology giants, or sold to other companies to show you advertisements that you have a higher probability of clicking on, and which have a higher probability of getting converted into a sale. This is one of the latest and most widely used applications of text analytics and mining.
Decision making is difficult. It is even more difficult when you have to answer to your shareholders as to why you took the decision and how you think that the decision will impact the company in a positive manner. Text mining really helps gather evidence and draw up charts and graphs to put information to back your gut feeling. Even analyzing petabytes of the organization’s internal as well as open source data becomes easy when using the software engines to power the hunt for strategic information. Only relevant information and data is extracted so that the people who lead can take the best decisions by going through only a few pages of information.
Writing content for blogs is one thing that an artificially created bot working on text analytics still can’t do. However, it can collect various information related to the topic that you need, along with latest news and most viewed articles on the subject to help you make a calculated guess as to how to form your article and what subtopics to add to it. This makes a significant difference when writing on topics that have huge volumes of preexisting data on the internet. This helps make your content informative and connect to previous articles and studies in the same field.
E-mails are still considered as the most official way of communication in most organizations. But it has a dark side that has only increased in the twenty-first century – spam. Out of every ten emails in my mailbox, at least nine are spam. Spams not only fill up space but also serve as an entry point for viruses, scams, and more. Companies are pushing hard to filter more and more spam by using intelligent text analytics as compared to the keyword matching used earlier, to filter out more spam emails and give the user a healthier experience.
In case you run a business that can grow on text scraping and text analytics, remember that data is power, and before you decide how to harness data, make sure you consult someone who has already used data to their benefit or helped others do so.