At the end of 2016, 90% of all data in the world had been created in the previous two years. Forty thousand searches are done on Google every second and, every minute, up to 300 hours of video are uploaded onto YouTube alone.

As the importance of data increases in our day-to-day lives, the problem is this: How do you make sense of it all? How do you find what is relevant to you, in as little time as possible? The media update team investigates the problem of big data, and how AI is helping brands to make sense of it all.
Manual capabilities in processing and allocating data only go so far. As the problem of Big Data grows at an exponential rate, new tools are needed to tackle the largest haystack that has ever existed.

The broad answer is AI. The specific answer, and what falls under the broad banner that is AI, is natural language processing (NLP) and, with that, machine learning.

How AI can solve the Big Data conundrum

AI is a branch of computer science that focuses on the creation of intelligent systems to perform and react like humans. As a field of AI, NLP allows computers to understand human language. With NLP, data processing companies, and especially document processing companies, can accurately process any type of text, from formal documents to conversational emails and even the slang that is used on social media.

"As a field of AI, NLP allows computers to understand human language"
This is because NLP solves a number of challenges that computers experience when they process text. Computers that use traditional processing methods cannot always identify all the people, places, events, and topics, better known as 'entities', that are mentioned in the text. They are also unable to recognise that some entities are referred to by synonyms and that some words have more than one meaning.

NLP engines overcome these challenges by using the mechanics of language to consider each word and its meaning in a text separately. This allows them to process, sort, categorise, identify, and even find relationships between entities.

Machine learning improves AI-powered text processing

NLP engines can be produced in a number of ways, with machine-learning-based techniques being some of the most efficient ways to do so. Machine learning allows companies that build NLP engines to save on time and resources.

Instead of employing subject matter experts to create static rules that NLP engines use to process data, companies can now feed their engines large amounts of data. Machine learning automatically creates algorithms based on this training data, creating its own rules for processing data – without the extensive help of an expert.

Machine learning allows data processing companies to efficiently improve their NLP engines to process any type of document. Machine learning is also what makes the extraction of entities from text possible, enabling companies to automatically tag who or what is discussed in the text, along with details about the entity.

In addition, machine learning creates algorithms that can recognise relationships between entities within vast amounts data. The algorithms can deliver insights from the data that would not have been possible to obtain through human analysis or rules-based computing.

Benefits of machine learning-based NLP engines for brands

The flexibility and automation that machine learning brings to NLP, combined with its ability to uncover new insights from data, makes it a major asset to data processing companies willing to develop these systems. Machine learning-based NLP engines also hold a number of benefits for the clients of data processing organisations, like brands and marketers.

According to Catherine Dabbs, a representative of data intelligence company Newsclip, this AI-powered technology can relieve much of the frustration marketers experience when they have to make sense of the data their campaigns and PR efforts generate.

"AI-powered technology can relieve much of the frustration marketers experience when they have to make sense of the data"
“Social media generates trillions of conversations every day, and brands are becoming increasingly concerned about losing touch with stories, especially when their reputations are at stake. Publishers contribute huge volumes of content to this ever-growing mountain of big data, and marketing departments are challenged to extract meaningful insights from the information.” 

“The convergence of AI-powered technology and Big Data is shaping the way in which businesses, including Newsclip, process information,” Dabbs explains. “The widespread availability of data is the most important shift towards the growth of AI, as more data means more learning and, ultimately, better AI.”

Greater insights into media and customer data

The ability to immediately analyse data is one of the most significant advantages that AI offers data processing companies. Dabbs notes that immediate access to data is imperative for businesses wanting to know what is going on in the media. “Information is processed in real time, giving businesses the most up-to-date information. This also allows businesses to be aware of any issues which may arise, as they happen.”

"The ability to immediately analyse data is one of the most significant advantages that AI offers data processing companies"
Sentiment analysis is another field where AI is making great strides. NLP extends the accuracy of sentiment analysis because it considers the meaning of every word in a text and more accurately interprets their meaning. It also determines the sentiment of every sentence, which can provide marketers with a more in-depth understanding of the sentiment surrounding their brands.

“Newsclip has recognised the potential that AI-powered technology has to extract accurate and highly valuable media intelligence from the data it gathers,” says Dabbs. “Our custom-built NLP engine is revolutionising the way we enrich the brand monitoring insights we deliver to clients and provides them with immediate, in-depth analysis of the mountains of data the media generates.”

AI, machine learning, and natural language processing have made possible what was only imaginable a few years ago: extracting value from an ocean of information, and converting Big Data into bite-sized, consumable pieces.

For more information on Newsclip, visit or get in touch with the company on Facebook, Twitter or LinkedIn.