Content Models with Attitude

Christina Sauper,  Aria Haghighi,  Regina Barzilay
MIT


Abstract

We present a probabilistic topic model for jointly identifying properties and attributes of social media review snippets. Our model simultaneously learns a set of properties of a product and captures aggregate user sentiments towards these properties. This approach directly enables discovery of highly rated or inconsistent properties of a product. Our model admits an efficient variational mean-field inference algorithm which can be parallelized and run on large snippet collections. We evaluate our model on a large corpus of snippets from Yelp reviews to assess property and attribute prediction. We demonstrate that it outperforms applicable baselines by a considerable margin.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-1036.pdf