NLP: how machine learning can make sense of unstructured data.
Mike Gunter explains how NLP is already a staple of everyday life
and its power to interpret messy human language data.
Uncovering the insights hidden in unstructured data can be a challenge, but skillful application of data science techniques like NLP allows for this messy data to be harnessed and understood.
Humans use spoken or written language to communicate with each other – that’s well known. But no matter how much care they take verbally or redrafting they do for written language pieces; humans by nature use error prone rules and illogical statements. Despite this, more often than not, we understand each other well.
Computers, on the other hand, communicate via machine code, which requires perfect structure to be understood. The result is a language barrier between the two. And it is not an easy task teaching machines to understand how we communicate.
Using technology and Machine Learning (ML) to simplify this process, Natural Language Processing (NLP) allows computers to more easily understand, interpret and analyse human language. The main goal is to make sense of the human language in a manner that is valuable and allows the business to drive decisions. Everything we write or speak carries huge amounts of information, and herein lies some of the most valuable insights. The topic we choose, our tone, our selection of words, everything adds information that can be interpreted, and therefore value extracted from it. In theory, we can understand and even predict human behaviour using that information through data science.
Data generated from chatbots, customer service emails, online forms, social media posts, feedback forms are all examples of unstructured data. Unstructured data does not fit neatly into the traditional row and column structure of relational databases and represents most data available in the actual world. It is messy and hard to manipulate. However, thanks to ML, it is no longer about trying to interpret a text based on its keywords, but about understanding the meaning behind those words – the semantics.
Today, NLP is all around us. Some of the more common uses include:
Computers, on the other hand, communicate via machine code, which requires perfect structure to be understood. The result is a language barrier between the two. And it is not an easy task teaching machines to understand how we communicate.
Using technology and Machine Learning (ML) to simplify this process, Natural Language Processing (NLP) allows computers to more easily understand, interpret and analyse human language. The main goal is to make sense of the human language in a manner that is valuable and allows the business to drive decisions. Everything we write or speak carries huge amounts of information, and herein lies some of the most valuable insights. The topic we choose, our tone, our selection of words, everything adds information that can be interpreted, and therefore value extracted from it. In theory, we can understand and even predict human behaviour using that information through data science.
Data generated from chatbots, customer service emails, online forms, social media posts, feedback forms are all examples of unstructured data. Unstructured data does not fit neatly into the traditional row and column structure of relational databases and represents most data available in the actual world. It is messy and hard to manipulate. However, thanks to ML, it is no longer about trying to interpret a text based on its keywords, but about understanding the meaning behind those words – the semantics.
Today, NLP is all around us. Some of the more common uses include:
- Analysing product reviews. e.g. what quality issues do we have and how can we alter our marketing?
- Helping chatbots and virtual assistants communicate and improve e.g. by knowing a customer wants to leave, we can automatically direct them to the retention team.
- Curating social website feeds e.g. the Facebook news feed algorithm understands your interests and shows you news & posts that you’re more likely interested in.
- Finessing spam filters e.g. helping understand what’s inside email content and decide if it’s junk.
- Language translation tools such as Google Translate.
- Checking the grammatical accuracy of your text and making suggestions in Microsoft Word documents.
- Personal assistants such as Siri and Alexa
- Predictive text on your phone or within Internet search.
- Sentiment analysis tools in data science. NLP will analyse customer interactions, such as social media comments and reviews to see customers are saying. Analysis of these interactions can help brands determine how well a marketing campaign is doing or monitor trending customer issues.
As we’ve seen above, we put our trust in NLP to keep our inboxes spam-free and our word documents grammatically correct. So why not use it to uncover the insights in your own unstructured data points?
It’s likely you are sat on a lot of data that could be worth understanding, with NLP being just one of the tools at Realise UNLIMITED’s disposal to do so. With lots of our clients, this data often helps bolster and finetune customer understanding and is a crucial tool for injecting personality and personalisation into subsequent marketing campaigns. We’ll be delivering a webinar on this topic imminently, so please do sign up to attend!
It’s likely you are sat on a lot of data that could be worth understanding, with NLP being just one of the tools at Realise UNLIMITED’s disposal to do so. With lots of our clients, this data often helps bolster and finetune customer understanding and is a crucial tool for injecting personality and personalisation into subsequent marketing campaigns. We’ll be delivering a webinar on this topic imminently, so please do sign up to attend!