New app can determine if email content is good, bad or neutral

Researchers at the University of Portsmouth in Britain have developed a new app that will color code your texts, tweets and emails, letting you know if the information they convey is good, bad or somewhere in between.

The scientists call it “sentiment analysis” and it is essentially an algorithm designed to determine whether a mobile communication will make you happy -- “Hey, we can have dinner tonight!” -- or bummed -- “I am so angry at you!” -- or indifferent -- “I need you to pick up the kids at 5.”

If the communique is positive, it will show up highlighted in green. If it is negative, it will show up in red. And if it is neither good nor bad, it will show up in blue.

The app comes “pre-trained,” but users can also teach it which messages they perceive as negative and positive by labeling messages as they come in. After all, one person’s bad news is another person’s best news of the day.


“The ultimate objective … is to make the user aware of the negative contents they receive so they are able to manage their stress in the best possible way,” Mohamed Medhat Gaber, who worked on the project, told the BBC.

So far, the technology has been tested only on a few Android phones, and the researchers have not announced plans to market the app.

Still, we were curious if knowing that you are facing a sea of negative emails -- even without opening those emails -- is a good way to manage stress.

“I think it is a colorful idea, but I’m not sure this is a helpful problem solver for employees,” said David Grossman of the Grossman Group, a consulting company that helps large companies manage internal messaging.
The Grossman Group recently conducted a survey of chief executives, middle managers and employees to determine what people say stresses them out the most about work emails; the answer is mostly bad etiquette.


According to middle managers, the most serious misuses of email are extraneous back-and-forth replies (34%), using email when a call or meeting would be better (32%), using reply-all (29%), poorly written emails (26%) and copying others unnecessarily (25%).

Now all the researchers need to do is come up with an algorithm that lets you know which of your emails are back-and-forth replies that are of no consequence to your own work.


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