Is Intelligent Content Analysis the Answer to Information Overload?
The web as we know it today is a supersaturated content network. It is extremely difficult to discover, track, and distribute digital content efficiently and effectively online. The amount of content being created online on a daily basis (text, images and video) and the wide range of channels distributing it, means it has become increasingly difficult to block out the noise and focus on relevant content.
Content online is growing at an alarming rate:
— Jonathan Cloonan (@CloonanJ) March 24, 2015
News websites, blogs, recommendation apps, aggregators, social platforms are all battling for readers and for content to be consumed on their platforms. Content has become a massive component of digital marketing strategies, but the sheer volume of content out there means it’s harder than ever to find relevant content.
Blocking out the noise
While it’s not an entirely new concept, Content Analysis, especially applied to digital content, is more relevant today than ever before because we’re just creating so much of it. We need the ability to gather content at scale and uncover relevant content. By curate, we’re talking about collecting new and popular content, extracting insight from it, deciding whether it’s relevant, to a specific need or just noise.
Traditionally content analysis was carried out by knowledgeable humans who would manually trawl through hundreds if not thousands of pieces of content looking for useful or interesting pieces. Up until recently we relied on Keyword search or tagging to make this process easier, but there was still a large amount of time wasted in the curation process.
Things are little different today
Advancements in and the democratization of Natural Language Processing, Image Recognition and Machine Learning have had a profound effect on how we continue to discover, consume and distribute content on the web.
Discovering content online is about listening to the web and grabbing relevant pieces of content from a massive amount of data sources. Information retrieval, Machine Learning and Semantic Search advancements now make it possible for machines to monitor content at scale. Intelligent systems can now listen to the web and automatically discover and recommend relevant or personalized content. They can learn what content is relevant to a specific need and automatically block sift through the noise to uncover what matters, without relying on keywords or constant human supervision.
Analyzing content is about extracting insight. Understanding content to a human level. Extracting topics or concepts, mentions of people, places, brands etc from text or knowing an image contains the face of a man or a view of a sunset. Natural Language Processing allows machines or software to do exactly that, to understand content. Another aspect of analyzing content is understanding how that content is consumed and shared. What platforms has it been circulated on, what news sites are covering it, how many likes and retweets does it have and who is consuming it.
Whether you’re a publisher, a marketer, a recommendation engine, a news outlet or an advertiser, relying on a team of human agents to discover and analyze content just isn’t good enough anymore. We need to embrace technological advances to work smarter and faster and keep on top of the expanding web of content online today.