Paper: A temporal model of text periodicities using Gaussian Processes

ACL ID D13-1100
Title A temporal model of text periodicities using Gaussian Processes
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

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.