Extracting Opinion Targets and Opinion Words from Online Reviews with Graph Co-ranking
Published 1 January 2014Open access
Kang Liu, Liheng Xu, Jun Zhao
Citations57
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TL;DR
This paper proposes a novel approach to collectively extract opinion targets and opinion words from online reviews with graph coranking, which constructs a heterogeneous graph to model two types of relations, including semantic relations and opinion relations.
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
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