Predicting Stances from Social Media Posts using Factorization Machines
作者： Naoaki Okazaki , Kentaro Inui , Kazuaki Hanawa , Akira Sasaki
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摘要: Social media provide platforms to express, discuss, and shape opinions about events issues in the real world. An important step analyze discussions on social assist healthy decision-making is stance detection. This paper presents an approach detect of a user toward topic based their stances other topics posts user. We apply factorization machines, widely used method item recommendation, model preferences from data. The experimental results demonstrate that users’ are useful therefore predict silent users.
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