Content models with attitude
Name
Barzilay-Content models.pdf
Size
473.45 KB
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Adobe PDF
Checksum (MD5)
3053adf43159c2494342a2e3c04eb924
Author(s)
Sauper, Christina Joan
Haghighi, Aria
Barzilay, Regina
Date Issued
June 2011
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
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://delivery.acm.org/10.1145/2010000/2002517/p350-sauper.pdf?ip=18.51.3.76&acc=OPEN&CFID=87070219&CFTOKEN=34670296&__acm__=1339176646_4a8b04d44fea48d556c322b59ff88ce8
