Software agents give out PR advice


By Duncan Graham-Rowe Governments and big business like to indulge in media spin, and that means knowing what is being said about them. But finding out is becoming ever more difficult, with thousands of news outlets, websites and blogs to monitor. Now a British company is about to launch a software program that can automatically gauge the tone of any electronic document. It can tell whether a newspaper article is reporting a political party’s policy in a positive or negative light, for instance, or whether an online review is praising a product or damning it. Welcome to the automation of PR. Till now, discovering whether the coverage you are getting is good or bad, negative or neutral has usually meant hiring a “reputation management” firm. Teams of people employed by the company will read through everything written about a chosen organisation, person, event or issue and report back on how favourable it is. As well as being expensive, this can be a long, slow process, says Nick Jacobi, director of research for the Corpora Software company in Surrey, UK. “There’s a massive information overload.” A single news agency may churn out more than eight articles each hour. That is almost 200 stories a day per news outlet. Previous attempts to automate this kind of analysis have used one of two techniques. In the first, called machine learning, a program is trained by being given thousands of articles already determined by a human reader to be positive or negative in tone. But learning in this way can lead to mistakes. For example, if a series of the training articles mentions bomb attacks on a mosque in Iraq, the program may incorrectly conclude that all other mentions of mosques are negative too. The alternative is the lexicon approach, in which certain words are classified as either positive or negative. But plenty of words can be both. “The plot was unpredictable” and “the steering was unpredictable” differ by just one word. Yet the word “unpredictable” has a positive connotation in the first example and a negative meaning in the second. And even if that problem is solved, just picking up on positive or negative words can also lead to mistakes, as is demonstrated by the sentence: “Everyone told me it was terrible, that I would hate it, but in the end it wasn’t at all bad”. So Corpora has come up with a program called Sentiment, which uses algorithms to tease out grammatical components, such as nouns, verbs and adjectives, and identify the subjects and objects of verbs. It can even analyse pronouns like “it”, “he” and “her” to work out what words or concepts they are referring to. Having an understanding of grammatical structure makes it possible to filter out words that are not relevant to the sentiment of the article, Jacobi says. So instead of assuming certain words, such as “unpredictable” or “rubbish”, are positive or negative it allows the structural context to disambiguate them. It does not get it right all the time, Jacobi admits, but then neither do people. Three expert readers are likely to agree about an article 85% of the time, and about 90% of non-experts will agree with this consensus. The Sentiment software agrees with the same expert consensus about 80% of the time. Sentiment was developed principally for Infonic, one of Corpora Software’s subsidiary companies, which provides clients with online media analysis of websites, chat rooms, bulletin boards and blogs. The company also hopes to use it to analyse the news for its clients. Sentiment will not take the humans out of the equation, says Orlando Plunket Greene of Infonic, because someone is still going to have to evaluate the software’s report on each article. But because the program will list items in terms of how positive, negative or neutral they are it is possible to skip to the most relevant items. “It will allow us to prioritise, and do the job much faster,” he says. While a person might be able to scan 10 articles an hour, Sentiment can zip through 10 a second. What makes this kind of analysis so challenging is that key words in a text often offer no clues as to what sentiment they carry. Some of the toughest challenges to comprehension, such as identifying irony and rhetoric,
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