Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews

Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 1754–1763, Sofia, Bulgaria, August 4-9 2013. c⃝2013 Association for Computational Linguistics Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews Kang Liu, Liheng Xu and Jun Zhao National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences {kliu, lhxu, jzhao}@nlpr.ia.ac.cn Abstract Mining opinion targets is a fundamen- tal and important task for opinion min- ing from online reviews. To this end, there are usually two kinds of methods: syntax based and alignment based meth- ods. Syntax based methods usually ex- ploited syntactic patterns to extract opin- ion targets, which were however prone to suffer from parsing errors when dealing with online informal texts. In contrast, alignment based methods used word align- ment model to fulfill this task, which could avoid parsing errors without using pars- ing. However, there is no research fo- cusing on which kind of method is more better when given a certain amount of re- views. To fill this gap, this paper empiri- cally studies how the performance of these two kinds of methods vary when chang- ing the size, domain and language of the corpus. We further combine syntactic pat- terns with alignment model by using a par- tially supervised framework and investi- gate whether this combination is useful or not. In our experiments, we verify that our combination is effective on the corpus with small and medium size. 1 Introduction With the rapid development of Web 2.0, huge amount of user reviews are springing up on the Web. Mining opinions from these reviews be- come more and more urgent since that customers expect to obtain fine-grained information of prod- ucts and manufacturers need to obtain immediate feedbacks from customers. In opinion mining, ex- tracting opinion targets is a basic subtask. It is to extract a list of the objects which users express their opinions on and can provide the prior infor- mation of targets for opinion mining. So this task has attracted many attentions. To extract opin- ion targets, pervious approaches usually relied on opinion words which are the words used to ex- press the opinions (Hu and Liu, 2004a; Popescu and Etzioni, 2005; Liu et al., 2005; Wang and Wang, 2008; Qiu et al., 2011; Liu et al., 2012). In- tuitively, opinion words often appear around and modify opinion targets, and there are opinion re- lations and associations between them. If we have known some words to be opinion words, the words which those opinion words modify will have high probability to be opinion targets. Therefore, identifying the aforementioned opin- ion relations between words is important for ex- tracting opinion targets from reviews. To fulfill this aim, previous methods exploited the words co-occurrence information to indicate them (Hu and Liu, 2004a; Hu and Liu, 2004b). Obviously, these methods cannot obtain precise extraction be- cause of the diverse expressions by reviewers, like long-span modified relations between words, etc. To handle this problem, several methods exploited syntactic information, where …

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