Content Models with Attitude

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 meanfield 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.

• Type: Journal
• Year: 2011
• Where: ACL
• Authors: Christina Sauper, Aria Haghighi, Regina Barzilay
• Keywords: ACL 2011, Baselines, Computational Linguistics, Inference Algorithm, Yelp