Can machines really learn sentiment?
With the rise of machine learning based AI data analysis, emotional intelligence is the next chapter in adaptive learning.
Emotional intelligence (EQ) is the next frontier for machine learning. Commentators have doubted computers could ever understand the sentiment behind the words people use. How could machines fathom sarcasm, irony or underlying meanings?
But in recent decades computer learning has accelerated, achieving feats that were once considered impossible. Machines have now passed the Turing test by imitating a real person, and beaten a world champion at ‘Go’, an ancient Chinese game which is more complex than chess.
In fact, machines not only understand sentiment, but they can process it on a vast scale – far more than people can.
EQ is already being used for commercial advantage in market research; most large companies use algorithms to scan comments about their business on social media. This allows them to track brand reputation, measure the effectiveness of new initiatives, and even help predict a potential crisis.
In fact, the question isn’t whether machines can understand sentiment and emotions, but how much can we benefit from it?
Unstructured data can breed misunderstanding or incomplete information when processed by human brains.
Around 80% of the world’s data is ‘unstructured’ – in words rather than numbers. We can understand what something means if we read it, but time is short. We can only process a minute fraction of everything we want to know. If other people need the information, either they have to read it too or we spend more time telling them.
But the problems don’t end there. What if we read it wrong? Or our colleague thinks it means something different? The reality is, we’re human. Sometimes we make mistakes, communicate badly, are influenced by our emotions, or simply have different opinions. By some counts, people only agree on sentiment around 60-65% of the time.
This means computers might actually be better – and more consistent – at understanding human sentiment than humans are. Machines are completely rational and standardised, and the amazing advantage of machine learning is that it’s always improving. People learn over time, but not at anything like the rate that computers do. In relative time terms, the evolution of machine intelligence is developing at a proportional faster rate than humans have.
The opportunities of using machine learning led sentiment analysis.
We need to capitalise on the possibilities of sentiment analysis now. Not only would we free up people to do more creative, unique work, but the amount of information we could comprehend would be colossal. The applications for machine intelligence are growing by the day, so the exponential value potential has the ability to change the world as we know it. Certainly our children and their children will react, interact and engage with a world that will be night and day different to ours. And they will have learned to exploit it better too.
The human limits of what we can understand means we’re missing out on so much. And businesses who capture the power of emotional intelligence in computers early stand to win big. Machine learning has the potential to do much more than categorise consumer reviews.
Temporall’s mission is not just to make an existing process more efficient, but to use machine learning to analyse something that has never been accurately measured before.
Our vision is that this amazing technology will let businesses understand their workplace culture like never before, and unlock the knowledge that will help them create the best possible environment for people’s health, happiness and productivity.
To me, that’s a truly human future.
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