Project Feels: Deep Text Models for Predicting the Emotional Resonance of New York Times Articles
Project Feels: Deep Text Models for Predicting the Emotional Resonance of New York Times Articles


In Project Feels, developed by the Data Science Group at the New York Times, we sought to predict how likely a given article was to evoke a range of emotions. We crowdsourced data from a pool of hundreds of thousands of articles. We modeled this data using a range of state-of-the-art text models. We used our model-predictions to create premium advertising spaces, and showed quantitative improvements in randomized control trials. Topics discussed will be: active learning, deep learning, Bayesian inference and causality.


Alex has worked as a data scientist at The New York Times since July 2014. His work has primarily involved text modeling for newsroom, product and advertising stakeholders to create advanced recommendation engines, perform automated information retrieval for journalists and sell premium ads. His work has been written about or featured in The New York Times, The Wall Street Journal, on NPR, and in Columbia Journalism Review, and at conferences, and he has earned a Masters in Data Science and a Masters in Journalism from Columbia University.

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