Content Models with Attitude
Christina Sauper, Aria Haghighi, Regina Barzilay · DSpace@MIT (Massachusetts Institute of Technology) · 2011
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 senti-ments 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 paral-lelized 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 con-siderable margin. 1