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Content models with attitude

Author(s)
Sauper, Christina Joan; Haghighi, Aria; Barzilay, Regina
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Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/
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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.
Date issued
2011-06
URI
http://hdl.handle.net/1721.1/74247
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, ACL HLT '11
Publisher
Association for Computational Linguistics
Citation
Sauper, Christina, Aria Haghighi, and Regina Barzilay."Content models with attitude." in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Portland, Oregon, June 19-24, 2011. pages 350–358.
Version: Author's final manuscript

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