Extracting Aspect Specific Opinion Expressions

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 627–637, Austin, Texas, November 1-5, 2016. c⃝2016 Association for Computational Linguistics Extracting Aspect Specific Opinion Expressions Abhishek Laddha IIT Delhi ∗ New Delhi, India, 110016 laddhaabhishek11@gmail.com Arjun Mukherjee Department of Computer Science, University of Houston, TX, USA arjun@cs.uh.edu Abstract Opinionated expression extraction is a cen- tral problem in fine-grained sentiment anal- ysis. Most existing works focus on either generic subjective expression or aspect ex- pression extraction. However, in opinion mining, it is often desirable to mine the as- pect specific opinion expressions (or aspect- sentiment phrases) containing both the as- pect and the opinion. This paper proposes a hybrid generative-discriminative framework for extracting such expressions. The hybrid model consists of (i) an unsupervised gener- ative component for modeling the semantic coherence of terms (words/phrases) based on their collocations across different documents, and (ii) a supervised discriminative sequence modeling component for opinion phrase ex- traction. Experimental results using Ama- zon.com reviews demonstrate the effective- ness of the approach that significantly outper- forms several state-of-the-art baselines. 1 Introduction Aspect based sentiment analysis is one of the main frameworks in opinion mining (Liu and Zhang, 2012). Most of the websites only display the ag- gregated ratings of products but people are more in- terested in fine-grained opinions that capture aspect specific properties in reviews. Therefore, it is desir- able to have a holistic approach to mine aspect spe- cific opinion expressions containing both aspect and ∗Research performed during author’s internship at Univer- sity of Houston opinion terms within the sentence context as a com- posite aspect-sentiment phrase (e.g., “had to flash firmware everyday”, “clear directions in voice” etc.) and further group them under coherent aspect cate- gories. Apart from knowing the key issues in prod- ucts that are often expressed via aspect-sentiment phrases, they are also useful in applications such as comparing similar products and summarizing their important features where it is more conve- nient to have the aspect-sentiment phrases rather than generic aspect/sentiment words lacking the nat- ural aspect opinion correspondence in the right con- text. They can also be applied to the various tasks such as sentiment classification, comparative aspect evaluations, aspect rating prediction, etc. The thread of research in (Brody and Elhadad, 2010; Titov and McDonald, 2008; Zhao et al., 2010; Mei et al., 2007; Jo and Oh, 2011) focus on extract- ing and grouping aspect and opinion words via gen- erative models but lack the natural aspect opinion correspondence (e.g., in the manner they appear in sentences). (Wang et al., 2016; Fei et al., 2016) can discover aspect specific opinion unigrams but does not focus on phrases. The thread on fine grained opinion expressions (Wiebe et al., 2005; Choi et al., 2006; Breck et al., 2007) focus on subjective expres- sion extraction which are generic instead of aspect specific. Formally, the task can be stated as follows: Given a set of reviews, for each sentence, s = (w1, . . . wn), with the head aspect …

Content truncated to 500 words