Digital marketing relies on good information. What are your conversion rates? Which email subject lines are the most effective? Where should we spend our ad budget?
The more you know about a particular channel, the better your campaigns will perform.
The modern world has made this communication possible. And, thankfully, the modern world is helping us to keep track of what’s being said.
“Artificial intelligence,” “machine learning,” and “deep learning” are three increasingly popular buzzwords, and each helps us to process large amounts of information. In this article, we’re going to dig into these basic AI concepts and see why they’re so valuable in making a large amount of social media data actionable.
It’s easy to get lots of information from social media. There are plenty of scrapers out there that’ll capture what’s said on each social platform. But can you actually do anything with it?
With the help of our AI technology, now you can.
John McCarthy, an earlier pioneer in the field of AI, defined artificial intelligence as “the science of making machines that can perform tasks that are characteristic of human intelligence.” This might include understanding language, translating content between languages, recognizing elements in images and speech, or making decisions.
Most people think of artificial intelligence as thinking and behaving just like people. All the excitement and fears about artificial intelligence today focus on this dystopian concept of “generalized AI,” even if none exist (for now).
Instead, most of the Artificial Intelligence systems developed by companies and researchers are applied AI, including machine learning systems. Applied AI works in a very limited field: it can perform extremely well on specific problems. For example, a machine that is great at recognizing logos in images, or a self-driving car, would fall into this category.
You use applied AI countless times each day without even realizing it: When you talk to Siri or Alexa, when you browse your recommended movies on Netflix, or when Facebook recommends users to tag in your freshly uploaded pictures.
Machine learning is a subfield of artificial intelligence and a way of creating problem-solving systems. Before the rise of machine learning, programmers manually coded instructions, using a certain input to obtain the desired output.
With machine learning, statistical techniques help us teach computers to learn without needing such a rigid set of rules. To do so, we show several examples – from a few hundred to several million – to our system until it eventually starts to learn over time and to answer (or predict) more accurately.
Machine learning systems are very narrow in their capabilities, often solving only one type of problem. This might be bidding for online advertising, detecting credit card purchase frauds, or even identifying cancerous skin cells.
Many can now reach or even outperform human experts at these tasks, and can do them at much larger scales.
Deep learning is one of many approaches to machine learning. This pioneering technology relies on complex systems called neural networks which mimic (at a very rudimentary level) the structure and function of the brain to perform pattern recognition: they are based on artificial neurons connected to each other. The networks are composed of several layers of these neurons to create complex architectures that enable the system to better capture the patterns to recognize. When you start stacking many of these neurons layers, your network is becoming “deep” and that’s why we use the term “deep learning”.
These systems have shown spectacular results with high accuracy and high reliability, and have therefore gained in popularity in recent years among data scientists.
When deep learning was conceptualized at the end of the 20th century, it didn’t get enough attention. Deep neural networks are very costly to train, and computers had low computational resources at the time.
They also perform better when there is a large amount of data to train them. That means megabytes or gigabytes of data. If you remember floppy disks, the most popular one could only handle few megabytes (Mo) at most, so you can imagine why it was expensive and hard for researchers or even industries to store large amounts of data.
Now that computer storage (hard drives and SSDs) are cheap and vastly more powerful (both CPUs and Graphical Processing Units), deep learning has gained a lot of hype among industries and researchers.
It is now possible for every individual, with the right computer, to train a basic deep neural network.
So those were your basic definitions. But why should you care?
Social media analysis relies on big data to gain more insights for your marketing strategy. The more you know about social media audiences, the better you can market your products.
But big data is only relevant if you can take advantage of that large volume of conversations. These conversations are spontaneous and unstructured. They’re highly variable, complex and often noisy – which makes it difficult to analyze, sort and categorize.
While you could manually pore over huge lists of posts to find answers to your questions, you can’t process this information without accurate automation.
Machine learning lets you scale your social media analysis to any amount of data – that could mean trillions of posts! And yet you can still easily keep up with consumer opinions and trends.
You can aggregate that data to find overall trends. But artificial intelligence can also be trained to highlight posts that are particularly valuable.
A simple example is the ability to distinguish automatically if the term “Orange” refers to the telecommunications company, the name of a city, or to the color. One of those is going to be highly valuable (if you’re in marketing for the phone company), while the others are just noise.
Machine learning systems are trained with example posts to recognize patterns in texts or images. They have the ability to interpret tiny nuances and can return the most relevant results to your questions with great accuracy.
As machine learning relies on examples to recognize patterns, it can uses examples of posts in any language to learn to categorize new posts as long as these posts are correctly annotated with the expected prediction.
The social web has become increasingly visual now with platforms such as Instagram, Snapchat or Pinterest. Posts on these platforms are mainly visual, and only a few hints are available in the content of the text.
So in the past, identifying what’s in these posts was virtually impossible.
Fortunately, that’s where deep learning comes to the rescue again. These systems can now recognize logos, faces, and objects, in both images and video. If you need to know when people are sharing your products on social media, image recognition is absolutely essential.
It can be hard enough to understand what different social posts mean just reading them one by one. Sarcasm, double meanings, and synonyms left older automated analysis behind.
While it’s obvious for humans to identify the intended sense of a word given the context (such as Orange), or to identify the tone if someone is being sarcastic (for sentiment analysis), this is not an obvious task for computers.
But machine learning now gives us highly accurate automated analyses. Complex models can be designed to understand the true meaning expressed in posts that could not be captured with traditional rule-based methods.
Machine learning is useful to recognize patterns in the language, images or in metadata. And we can now rely on these patterns to sort posts into predefined categories.
However, these patterns can also be used to detect new trends or topics that do not fit into a pre-existing set of values. The algorithm looks for interesting structures and tries to group similar examples. These machine learning techniques are called “unsupervised,” and they highlight as a discovery tool or when new results fall outside what was expected.
Proper social media analysis requires the right tools. Social media is awash with insightful information. And there are too many conversations happening every day for you to possibly monitor them all manually.
Artificial intelligence makes your social media analysis more powerful, and more accurate. At Linkfluence, we use machine learning algorithms in all our data enrichment steps to provide our customers with reliable and accurate insights on their brands, products, and ambassadors.
Want to try it for your brand? Talk to us today: