Data is now more important than ever – especially to businesses with The Economist recently dubbing it as “the oil of the digital era”. It may be hard for some to understand the comparison, as data is measured in bytes, not gallons, and bytes are not a physical thing (as such). Many find it challenging to connect with data and its importance, as they cannot see what they are generating, gathering and storing – but this does not decrease its importance.
Natural language processing (NLP) has also become more important than ever recently, as not only does it help us teach computers to understand how we speak, but it can also help us to make sense of patterns within a text. This then leads us to understand the sentiment behind the text and also gives us a clearer understanding of stylistic devices used in literature as well.
Although we believe that natural language processing is going to become even more important to businesses over the next ten years, there are some businesses that are using it already for tasks such as:
#1 Text Extraction
If you use Microsoft Office on a daily basis you know that if you press Ctrl + F you can search through your Word document to find a specific word or sentence. The easiest way to think about NLP is to equate it to this – NLP can search through text instantly, and extract the important information that you need. Not only that, but the algorithms associated with NLP can also find connections between the passages of text and generate statistics relating to them.
#2 Text Classification
So, we have seen that NLP can extract text from passages of information. Additionally, it can organise text into categories. From a personal point of view, NLP text classification is what is used to detect patterns which are heavily used by spammers, and so help to keep your email inbox free from spam. Text classification is also heavily used in the insurance industry as a way of organising and categorising company contracts.
However, NLP is not just a tool used for looking at keywords within passages of text. It can also help us understand the meaning behind them:
#3 Sentiment Analysis
Text extraction and classification is about understanding and classifying text. Sentiment analysis builds on this by applying knowledge of subtext – which comes in particularly useful when considering customer satisfaction. Amazon, for example, is using NLP to help them keep track of the customer service levels of their sellers, and Deutsche Bahn is using it to delve deeper into the reasons why people are unhappy with their service.
One company who is really investigating how far we can go with sentiment analysis is Facebook. Instead of merely tracking satisfaction levels, they are examining hate groups that have been created on their platform, then using this data to try and prevent them from mobilising.
Many businesses are still only scratching the surface of what NLP can do, and as machine learning and technological developments such as quantum computing keep advancing, we could see NLP take an even bigger role in our daily lives:
#4 Personal AI Assistance
Most households now have personal AI-based assistants – such as Amazon’s Alexa or Google Home, and so we are all used to talking to our phone or smart device when we want to set a reminder or an alarm. However, as the use of these devices increases, so will our need for them to understand us better, which means computers now need to be able to communicate with us in a more natural way.
This advancement in talking and texting with machines in the same way we communicate with our friends and family is not just limited to voice technology, it also helps with:
Chatbots have become more common on websites recently as companies have started to realise they can answer most of their customers’ frequently asked questions through the use of algorithms and chatbots. They save companies money as they do not need to recruit as many customer service staff, and are often so realistic that many people do not realise they are engaging with an algorithm rather than a person.
This is largely due to the fact that machine learning methods have allowed computers to understand what people mean, even when they haven’t put it into so many words.
Natural Language Processing (NLP) is used in many Big Data jobs – including Big Data Engineer, DevOps Engineer, business intelligence and so on. If you are interested in the use of NLP in the future of technology, then we may have just the role for you. Please take a look at our latest Big Data opportunities, or get in touch with one of our consultants to find out more.