The good news is it needs users’ permission in order to work. Álvaro Ortigosa and his team from the National Centre of Excellence in Cybersecurity at the Autonomous University of Madrid made the app, which analyses messages using two algorithms.
The first uses language analysis to work out how emotionally charged an update is, rating it as positive, negative, or neutral. The second compares this with the emotional content of other recent updates, thus understanding what kind of a mood someone’s in (and spotting any sudden changes). The idea is that over time the app learns to replicate the analysis a human would when reading the same message (whether that’s ‘She seems sad’ or ‘STOP SPELLING IT “HUN”’, presumably).
But the aim isn’t just to collect info on how good/bad/indifferent people feel. The researchers think SentBuk could be used in e-learning environments, providing feedback tutors would otherwise get from looking at students about how enthusiastic and engaged they’re feeling (or not). They say this could allow professors to adjust their study plans accordingly: that if, for example, a consistent number of students are in a bad mood, it could indicate the need for a new approach (less homework?)
The trouble is, this clearly encourages students to update Facebook when they should be working, which a lot of employers are going to frown on once they’ve graduated. Some potential users are going to find it (incredibly) invasive. And, perhaps worst of all, SenkBuk doesn’t tell tutors what to do if their students feel negative most or all of the time…
Image via Pixabay.
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