Clustering for Simultaneous Extraction of Aspects and Features from Reviews

Proceedings of NAACL-HLT 2016, pages 789–799, San Diego, California, June 12-17, 2016. c⃝2016 Association for Computational Linguistics Clustering for Simultaneous Extraction of Aspects and Features from Reviews Lu Chen1∗, Justin Martineau2, Doreen Cheng2, Amit Sheth1 1 Kno.e.sis Center, Wright State University Fairborn, OH 45435 USA 2 Samsung Research America, Silicon Valley San Jose, CA 95134 USA {chen, amit}@knoesis.org justin.m@samsung.com doreenycheng@yahoo.com Abstract This paper presents a clustering approach that simultaneously identifies product features and groups them into aspect categories from on- line reviews. Unlike prior approaches that first extract features and then group them into cat- egories, the proposed approach combines fea- ture and aspect discovery instead of chaining them. In addition, prior work on feature ex- traction tends to require seed terms and fo- cus on identifying explicit features, while the proposed approach extracts both explicit and implicit features, and does not require seed terms. We evaluate this approach on reviews from three domains. The results show that it outperforms several state-of-the-art methods on both tasks across all three domains. 1 Introduction If you are thinking of buying a TV for watching football, you might go to websites such as Amazon to read customer reviews on TV products. How- ever, there are many products and each of them may have hundreds of reviews. It would be helpful to have an aspect-based sentiment summarization for each product. Based on other customers’ opinions on multiple aspects such as size, picture quality, motion-smoothing, and sound quality, you might be able to make the decision without going through all the reviews. To support such summarization, it is essential to have an algorithm that extracts product features and aspects from reviews. ∗This author’s research was done during an internship with Samsung Research America. Features are components and attributes of a prod- uct. A feature can be directly mentioned as an opin- ion target (i.e., explicit feature) or implied by opin- ion words (i.e., implicit feature). Different feature expressions may be used to describe the same aspect of a product. Aspect can be represented as a group of features. Consider the following review sentences, in which we denote explicit and implicit features in boldface and italics, respectively. 1. This phone has great display and perfect size. It’s very fast with all great features. 2. Good features for an inexpensive android. The screen is big and vibrant. Great speed makes smooth viewing of tv programs or sports. 3. The phone runs fast and smooth, and has great price. In review 1, display is an explicit feature, and opinion word “fast” implies implicit feature speed. The task is to identify both explicit and implicit fea- tures, and group them into aspects, e.g., {speed, fast, smooth}, {size, big}, {price, inexpensive}. Many existing studies (Hu and Liu, 2004; Su et al., 2006; Qiu et al., 2009; Hai et al., 2012; Xu et al., 2013) have focused on extracting features with- out grouping them into aspects. Some methods have been proposed to group features given that feature expressions have been identified …

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