  Inferring topics from the overwhelming amount of short texts becomes a
critical but challenging task for many content analysis tasks, such as content
charactering, user interest profiling, and emerging topic detecting. Existing
methods such as probabilistic latent semantic analysis (PLSA) and latent
Dirichlet allocation (LDA) cannot solve this prob- lem very well since only
very limited word co-occurrence information is available in short texts. This
paper studies how to incorporate the external word correlation knowledge into
short texts to improve the coherence of topic modeling. Based on recent results
in word embeddings that learn se- mantically representations for words from a
large corpus, we introduce a novel method, Embedding-based Topic Model (ETM),
to learn latent topics from short texts. ETM not only solves the problem of
very limited word co-occurrence information by aggregating short texts into
long pseudo- texts, but also utilizes a Markov Random Field regularized model
that gives correlated words a better chance to be put into the same topic. The
experiments on real-world datasets validate the effectiveness of our model
comparing with the state-of-the-art models.
