“Tony Abbott’s laugh is a little bit terrifying, but at least he can laugh.” One point for Tony.

This is how computers are determining whether our political leaders are in or out of favour with the public. Chunks of text are fed into a program and each tweet, Facebook status, blog post or news article is given a positive or negative stamp. It’s all totalled up and you end up with a single figure between -100 and 100. They call it sentiment analysis.

But are computers smart enough to understand the nuances of language, from cultural differences to sarcasm and irony? Or is sentiment analysis nothing but snake oil for marketing agencies to sell to the many corporations eager to get on the social media bandwagon, but unable to even locate the wagon in the car park?

Brand management is where most sentiment analysis is currently happening. Chocolate milk makers, fearing their product will be seen as a daggy school-related drink, are monitoring kids on social networks to see how their product is being talked about. Car companies are monitoring how many Facebook groups have been setup about their brand – both positive and negative – as well as how many people are in the groups and whether they’re making positive or negative statements.

Corporations love being talked about, and being talked about in social media circles is an enticing way to be in the headspace of the lucrative Gen Y-ers. There is a rush to know how to react when things go wrong, or to control what is being said. Sentiment analysis offers a foothold into this area, a modern day media monitoring service sans humans. The truth is that the best sentiment analysis providers claim to provide only a 70-80 per cent level of accuracy.

It is still a new and experimental area, so despite the low accuracy rate the technology shouldn’t be completely dismissed. As trivial as the above commercial uses may sound, sentiment analysis has the potential to provide analysis of human reactions in ways that online polls or questionnaires cannot, especially once the understanding of language, and therefore the levels of accuracy, improve.

For instance, a ministerial staffer could use the tool to gauge opinion on a new policy across blogs, news sites and social media. Once the machine has categorised the data into specified topics, the staffer could then confirm the analysis is correct. It’s machine-assisted work, rather than machines having the final say.

NICTA’s prototype is highly customised for each individual project with algorithms being tailored to the needs of the project. Gray does insist it’s still a tool for expert users due to the complicated nature of semantic technology.

Despite this, it’s still remarkable to see how far computers have come with analysing the complex interconnected world of human discourse. While we may not be living in George Jetson-land just yet, who knows how close we’ll be in five to 10 years. Now, that reminds me, who out there is working on a jet pack for me?Read more at www.abc.net.au