We propose a new pooling technique for topic modeling in Twitter, which groups together tweets occurring in the same user-to-user conversation. Under this scheme, tweets and their replies are aggregated into a single document and the users who posted them are considered co-authors. To compare this new scheme against existing ones, we train topic models using Latent Dirichlet Allocation (LDA) and the Author-Topic Model (ATM) on datasets consisting of tweets pooled according to the different methods. Using the underlying categories of the tweets in this dataset as a noisy ground truth, we show that this new technique outperforms other pooling methods in terms of clustering quality and document retrieval.
Download Full PDF Version (Non-Commercial Use)