We've come a long way since the early days of digital assistants. Now, natural language processing can help take user interactions to a whole new level and drastically improve their experience.
Market and Markets predicts that the global NLP market will skyrocket to over $49 billion by 2027. That's a remarkable CAGR of 25.7%.
If you're not sure how NLP will continue to benefit users, look at how quickly Google has leveraged it with its Assistant tool and how advances in technology have made mobile devices exponentially easier to use.
That’s the goal of NLP – to massively improve user experience (UX).
Think back – way back – to Star Trek IV . Remember the scene where Scotty sat in front of a computer and said the keyword “Computer”? He expected the computer to respond to his voice because that's what computers do. Only when he remembered that he had gone back in time, to a period where technology was not as advanced as in his timeline.
That's where we are now. Instead of Scotty (or us) having to pick up the mouse and talk to it or type on a keyboard, we simply talk to our devices and they respond. We see this in everyday life, not just with Google Assistant and Siri, but also in our automobiles. We speak commands and technology obeys.
But NLP doesn't want to limit itself to commands. Instead, you want the interaction between humans and computers to be as natural as possible (similar to conversational interaction with Google Assistant).
And NLP goes beyond the simple act of commands and conversations with a mobile device. There are several applications of NLP that have far-reaching implications for both humans and technology.
Sentiment Analysis
Sentiment analysis is focused on giving technology the ability to determine the emotional tone or feeling expressed by a human being. Applications of this are very important for businesses as they can empower chatbots to better determine how to help customers more effectively by judging feedback and attitudes towards a product or service.
If a customer said something like “Of course, your product really helped me,” but did so in a very sarcastic way, the goal of NLP would be to be able to discern that statement from an honest statement.
Sentiment is a crucial aspect of customer service and support. Integrating NLP with machine learning and deep learning algorithms improves tool accuracy and enhances NLP applications, contributing to the development of advanced human-centric technology. Statistical machine learning techniques can identify parts of speech, entities, sentiments, and aspects of text. Contact a Machine Learning Development Company to learn more about their contribution to NLP products.
Language Translation
Another very important aspect of NLP is the ability to translate languages in real time. This is becoming increasingly important as technology continues to “shrink” the world. With companies having to serve customers in every region of the world, the ability to quickly translate languages can make the difference between a good user experience and a bad one.
For example, if your company is based in India and your support team has limited knowledge of the English language, they will have difficulty helping English-speaking users. With NLP involved, this translation is not only instantaneous, it is accurate.
Extracting information
Imagine you have a collection of unstructured data and you need to make sense of it. Not everyone has the skills needed for such a task, but NLP certainly does. Because NLP is perfectly adept at identifying and extracting entities, relationships, and facts from documents, it is a great option for any business that relies on data analysis. And since NLP-powered technology is exponentially faster at extracting data than a human, it makes perfect sense for large companies (which rely on large amounts of data) to employ this technology.
By using NLP to extract information, your business will be better equipped to understand your users' needs and wants and identify patterns to improve their experience. A technology closely linked to UX and UI is Java. Now, if you are wondering what you can do with Java in this regard, know that there are several Java libraries that developers can use to build chatbots, and it is possible to use Java libraries for language analysis to extract insights from the results.
Classification
At some point, your company will need to be able to quickly (and reliably) classify documentation so that it is easier to analyze and consume. A prime example of how NLP can be used to vastly improve text classification is spam detection. If your company provides email services to customers, their experience will be drastically improved if you can reliably, consistently, and quickly classify any email that is spam so that users don't have to deal with it.
Unlike email clients that are unreliable at detecting spam, NLP is remarkably successful. If you can provide improved spam filtering for your users, their experience will be considerably improved, which will be reflected in their opinions and reviews of your product.
All of these things are part of NLP, but at the heart and soul of this technology is language, especially the conversational kind. And NLP has advanced so much that some say the Turing Test should be modernized for 21st century AI . The Turing Test is a test to determine a machine's ability to exhibit intelligent behavior. NLP makes it possible to pass this test much more easily, which not only means that the test may not be sufficient for modern technology, but it also means that NLP has surpassed previous incarnations of AI.
This equates to much better UX from almost every angle. And if your company is looking to improve or adopt UX best practices, NLP could be the technology you're missing.
Publication date: September 18, 2023
Source: BairesDev
