Social media is like a tap of golden data that when opened can unlock a wide variety of value for organizations ranging from discovering trends that impact shipping and logistics or complaints that directly indicate hardware or infrastructure issues. Social media is becoming so much more than a medium to communicate and will play a critical role in guiding businesses in the future, especially since technologies used to analyze social conversations have been revolutionized over the past couple of years.

In particular, recently there was big hype around using artificial intelligence (AI) for everything under the sun, but in reality most applications of AI weren’t functional, including early integrations of the technology to analyze social media. That’s where a subset of AI, machine learning, started to kick in.

According to Wikipedia, “Machine learning is a field of computer science that gives computer systems the ability to ‘learn’ (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.” Essentially, machine learning has the power to learn and is far more effective than conventional, static programming though it’s still not the holy grail and we learned that early on.

Our research and development on social media analytics has been focused on using deep learning to go above and beyond what’s possible with older machine learning techniques in favor of a more robust, effective approach. The linguistics and unpredictability of social media have shown us that deep learning is truly the way to go, which is why our social media listening and analytics platform as well as other tools we’re developing have deep learning deeply embedded.