Extracting Opinion Targets and Opinion Words from Online Reviews with Graph Co-ranking

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 314–324, Baltimore, Maryland, USA, June 23-25 2014. c⃝2014 Association for Computational Linguistics Extracting Opinion Targets and Opinion Words from Online Reviews with Graph Co-ranking Kang Liu, Liheng Xu and Jun Zhao National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China {kliu, lhxu, jzhao}@nlpr.ia.ac.cn Abstract Extracting opinion targets and opinion words from online reviews are two fun- damental tasks in opinion mining. This paper proposes a novel approach to col- lectively extract them with graph co- ranking. First, compared to previous methods which solely employed opinion relations among words, our method con- structs a heterogeneous graph to model two types of relations, including seman- tic relations and opinion relations. Next, a co-ranking algorithm is proposed to es- timate the confidence of each candidate, and the candidates with higher confidence will be extracted as opinion targets/words. In this way, different relations make coop- erative effects on candidates’ confidence estimation. Moreover, word preference is captured and incorporated into our co- ranking algorithm. In this way, our co- ranking is personalized and each candi- date’s confidence is only determined by its preferred collocations. It helps to improve the extraction precision. The experimen- tal results on three data sets with differ- ent sizes and languages show that our ap- proach achieves better performance than state-of-the-art methods. 1 Introduction In opinion mining, extracting opinion targets and opinion words are two fundamental subtasks. Opinion targets are objects about which users’ opinions are expressed, and opinion words are words which indicate opinions’ polarities. Ex- tracting them can provide essential information for obtaining fine-grained analysis on customers’ opinions. Thus, it has attracted a lot of attentions (Hu and Liu, 2004b; Liu et al., 2012; Moghaddam and Ester, 2011; Mukherjee and Liu, 2012). To this end, previous work usually employed a collective extraction strategy (Qiu et al., 2009; Hu and Liu, 2004b; Liu et al., 2013b). Their intuition is: opinion words usually co-occur with opinion targets in sentences, and there are strong modifi- cation relationship between them (called opinion relation in (Liu et al., 2012)). If a word is an opinion word, other words with which that word having opinion relations will have highly proba- bility to be opinion targets, and vice versa. In this way, extraction is alternatively performed and mu- tual reinforced between opinion targets and opin- ion words. Although this strategy has been widely employed by previous approaches, it still has sev- eral limitations. 1) Only considering opinion relations is in- sufficient. Previous methods mainly focused on employing opinion relations among words for opinion target/word co-extraction. They have in- vestigated a series of techniques to enhance opin- ion relations identification performance, such as nearest neighbor rules (Liu et al., 2005), syntactic patterns (Zhang et al., 2010; Popescu and Etzioni, 2005), word alignment models (Liu et al., 2012; Liu et al., 2013b; Liu et al., 2013a), etc. How- ever, we are curious that whether merely employ- ing opinion relations …

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