Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
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 | 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 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. 1 Introduction Online product reviews have become an increasingly valuable and influential source of information for consumers. Different reviewers may choose to comment on different properties or aspects of a product therefore their reviews focus on different qualities of the product. Even when they discuss the same properties their experiences and subsequently evaluations 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 retailers often use simple aggregation mechanisms to represent the spectrum of user sentiment. For instance product pages on Amazon prominently display 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
