A temporal model of text periodicities using gaussian processes


Temporal variations of text are usually ig-nored in NLP applications. However, text use changes with time, which can affect many applications. In this paper we model peri-odic distributions of words over time. Focus-ing on hashtag frequency in Twitter, we first automatically identify the periodic patterns. We use this for regression in order to fore-cast the volume of a hashtag based on past data. We use Gaussian Processes, a state-of-the-art bayesian non-parametric model, with a novel periodic kernel. We demonstrate this in a text classification setting, assigning the tweet hashtag based on the rest of its text. This method shows significant improvements over competitive baselines.

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oaioai:CiteSeerX.psu: time updated on 10/29/2017

This paper was published in CiteSeerX.

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