Tracking the Curve: Analyzing the Emotional Response to COVID-19

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By Sarah Binns

Hashtags like #covid19 and #coronavirus help us stay up to date on the developments of the new coronavirus pandemic. But beyond breaking news, tweets also offer a glimpse into the emotional side of the COVID-19 crisis. Are people feeling worried, sad, or confident about the virus? Xiangliang Zhang, an associate professor of computer science at King Abdullah University of Science and Technology (KAUST), wants to find out. Xiangliang runs the Machine Intelligence & kNowledge Engineering (MINE) group at KAUST where she builds computer models that identify Twitter users’ interests and track how they evolve. Now she’s turning her attention to COVID-19 and using machine learning to track the changing emotional response to the global crisis.

She and her team have collected millions of tweets from the last three months in English, Italian, French, Spanish, and Arabic and are using deep learning models to try and identify the emotions conveyed in the tweets. They built specialized models for each of the five languages and trained the models on the tweet data sets until the models could accurately identify how the author of the tweet felt about coronavirus. “We have very accurate models for English and Spanish, but our Arabic model isn’t very good. We’re now looking at how we can use the pre-trained English and Spanish models to help the Arabic model,” says Xiangliang. “This is an advanced topic in machine learning called transfer learning and also recently studied as meta learning.”

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