Chloe Green of Information Age reports, “The worldwide web is an unprecedented source of market intelligence that, in theory, allows businesses to analyse public opinion automatically. But human communication is complex. Context, slang, varying dialects and other foibles make it extremely difficult for a computer to extract what someone means from what they write online. Professor Arno Scharl, of MODUL University Vienna’s Department of New Technology, has set out to crack this conundrum with an approach that combines automated analysis with human intuition. And, he claims, his team is making significant strides.”

His approach is made up of two components. The first is an automated web content aggregator called the extensible Web Retrieval Toolkit, or eWRT, which uses semantic analysis to identify content, such as social media messages or online news stories, that are relevant to a particular topic and detects their sentiment. Scharl claims that eWRT can achieve both 80% recall – i.e. it will identify 80% of the relevant content on the web – and 80% precision in sentiment analysis – i.e. it will correctly identify the sentiment in 80% of the content. This, he says, is a significant improvement on commercial tools that may have high precision but low recall rates, or vice versa.”

Read more here.

Image: Courtesy MODUL University Vienna