Extracting Opinion Targets and Opinion Words from Online Reviews with Graph Co-ranking
Kang Liu, Liheng Xu, Jun Zhao · 2014
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