Probabilistic topic models (Blei et al., 2003) are broadly used to uncover the hidden topics of tweets, since the low-dimensional semantic representation is crucial for many applications, such as product recommendation (Zhao et al., 2014), hashtag recommendation (Ma et al., 2014), user interest tracking (Sasaki et al., 2014), sentiment analysis (Si et al., 2013).
the hyperparameters are set as follows: for all the models, we set α = 50/K, β = 0.01; for twitter-LDA, twitterUB-LDA and twitter-BtM, we set γ = 0.5.