Content Models with Attitude

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 350–358, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Content Models with Attitude Christina Sauper, Aria Haghighi, Regina Barzilay Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology csauper@csail.mit.edu, me@aria42.com, regina@csail.mit.edu 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 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 Introduction Online product reviews have become an increasingly valuable and influential source of information for consumers. Different reviewers may choose to com- ment on different properties or aspects of a product; therefore their reviews focus on different qualities of the product. Even when they discuss the same prop- erties, their experiences and, subsequently, evalua- tions of the product can differ dramatically. Thus, information in any single review may not provide a complete and balanced view representative of the product as a whole. To address this need, online re- tailers often use simple aggregation mechanisms to represent the spectrum of user sentiment. For in- stance, product pages on Amazon prominently dis- play the distribution of numerical scores across re- Coherent property cluster + The martinis were very good. The drinks - both wine and martinis - were tasty. - The wine list was pricey. Their wine selection is horrible. Incoherent property cluster + The sushi is the best I’ve ever had. Best paella I’d ever had. The fillet was the best steak we’d ever had. It’s the best soup I’ve ever had. Table 1: Example clusters of restaurant review snippets. The first cluster represents a coherent property of the un- derlying product, namely the cocktail property, and as- sesses distinctions in user sentiment. The latter cluster simply shares a common attribute expression and does not represent snippets discussing the same product prop- erty. In this work, we aim to produce the first type of property cluster with correct sentiment labeling. views, providing access to reviews at different levels of satisfaction. The goal of our work is to provide a mechanism for review content aggregation that goes beyond nu- merical scores. Specifically, we are interested in identifying fine-grained product properties across reviews (e.g., battery life for electronics or pizza for restaurants) as well as capturing attributes of these properties, namely aggregate user sentiment. For this task, we assume as input a set of prod- uct review snippets (i.e., standalone phrases such as “battery life is the best I’ve found”) rather than com- plete reviews. There are many techniques for ex- …

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