If you’ve ever asked Amazon’s Alexa or Apple’s Sir and received an answer, you’ve experienced natural language processing (NLP). Your device hears you speak, understands your intention, and performs the operation in about four seconds. You know it worked because it tells you – in a well-worded and correctly uttered human phrase.
Natural language processing is about how computers understand, interpret, and function human language. The technology is not new, but it is growing rapidly due to the rapid development of computing and easier access to big data.
What is NLP?
Natural language processing (NLP) is an area at the crossroads of computer science, artificial intelligence, and linguistics. The goal is for computers to process or understand natural language to perform language translation and answer questions.
With the advent of speech interfaces and chat robots, NLP is one of the most critical information age technologies, which is an integral part of artificial intelligence. Fully understanding and representing the meaning of language is a challenging goal. Why? Because human language is unique.
What’s Unique About Human Language?
Human language is a system specially designed to convey the meaning of a speaker/writer. It is not just an environmental signal, but a deliberate communication. Also, it uses coding that children can learn quickly; it is also changing.
Human language is mostly a discrete/symbolic/categorical signalling system, presumably due to higher signal reliability.
Category symbols of a language can be coded as a communication signal in several ways: sound, gesture, writing, images, etc. Human language is capable of them.
Human languages are ambiguous (unlike programming and other official languages); thus, the representation, learning, and use of linguistic/situational/contextual/verbal/visual knowledge of the human language are involved.
3 Most Common Ways Businesses Put NLP Into Practice
1. Address Customer Pain
Experienced consumers express their complaints (and sometimes praise) online, which is why monitoring a brand’s reputation is so important. Discovering what’s said about your business or your products on social media and elsewhere is an easy way to understand a customer’s voice.
However, manual analysis is almost impossible when there is more data than ever before, and artificial intelligence helps. Using sensory analysis, companies use text analysis and natural language processing to understand the emotions or meaning behind words.
Whether it recognizes your brand’s social mentions, discovering negative reviews, or simply diving into public opinion, knowing what people are saying about your company – and why – allows you to manage a strategy and create campaigns that better meet their needs.
The result of the sensory analysis can have profound consequences. As part of Syracuse University’s Natural Language Processing class, three students built a prototype emotional analyser to measure the media’s mood against Donald Trump. The model, which analysed thousands of articles about Donald Trump, elected by the then president, can help future political figures make better media strategic plans.
2. Gather Market Intelligence
Know what your competitors are doing, and more broadly, what your industry, in general, is, helping you develop an effective business strategy. However, most of the data collected today is unstructured, meaning that it is generated from conversations on social networks, via e-mail, and even when communicating with customer service representatives.
Understanding how competitors, customers, and the market interact is often buried in the text, infographics, and images in news, reports, SEC records, and company websites. Natural language processing helps companies to understand this information quickly and extensively through text extraction and categorization. For example, discovering timely news about a merger can have a significant impact on business decisions.
3. Reduce Customer Frustration
Virtual help dramatically improves the customer experience. With available self-service digital solutions, consumers avoid long waiting times and receive real-time answers to their most pressing problems. As NLP technology improves, traditional robots will be replaced by hybrid robots that sometimes don’t know how to answer questions fully.
NLP-enabled assistants who combine virtual and human support into one give an even better customer experience, quickly delivering a machine-led conversation to a person who doesn’t understand what the customer wants. The key to ensuring meaningful communication and an award-winning customer experience is to forward chats directly to the customer service representative before frustration arises.
Like many of today’s brands, Coca-Cola has integrated a virtual help program with its customer service department to meet consumer needs better. The Ask Coca-Cola assistant is said to be successfully conducting 30,000 conversations per month, reducing the need to communicate by phone.
Large Organizations are Expected to Register a Significant Growth
Large organizations are one of the leading drivers and investors in the NLP market. As these organizations increasingly adopt deep learning through guided and uncontrolled machine learning technologies for various applications, the use of NLP is likely to increase further. Cost and risk are some key factors driving the use of these technologies in large organizations.
Also, many small businesses depend on large companies. This is due to some extensive platform offerings from Amazon, Google, or Microsoft that are too general to be used in industries.
Most large end-user organizations in various industries mainly use these technologies with high-quality add-ons such as WooCommerce variable pricing to improve their internal and external operations. Moreover, the ROI of technology is not always in financial terms, so most small organizations risk investing.
Additionally, social media companies also use text analysis and NLP technology techniques, such as political reviews and hate speech, to monitor and track social media activities. Platforms like Facebook and Twitter manage published content using these tools. Demand for implementing information extraction products is also expected to increase as Internet data grows to be more critical for effective marketing and decision-making. In the coming years, mobile phone calls should change the market and business sector.
Many large European retailers used NLP and text mining to analyse customer reactions and hired a dedicated AI team in Ukraine to build the solution. By integrating the mood analyser into their processes, these retailers increase customer loyalty, cause business change, and achieve appropriate returns from sales and market investments.
As skills development improves, companies will use artificial intelligence technologies, such as NLP, to better understand customer intentions through mood analysis, understand unstructured data, and alleviate customer frustration.
With natural language processing technology, language data can be analysed faster than people, without prejudice, fatigue, or holiday. For daily data mines, the ability to thoroughly examine the text is distinguished from any source.