Co Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment Model
Mining opinion targets and opinion words from online reviews are important tasks for fine grained opinion mining, the key component of which involves detecting opinion relations among words. To this end, this paper proposes a novel approach based on the partially supervised alignment model, which regards identifying opinion relations as an alignment process. Then, a graph based co ranking algorithm is exploited to estimate the confidence of each candidate. Finally, candidates with higher confidence are extracted as opinion targets or opinion words. Compared to previous methods based on the nearestneighbor rules, our model captures opinion relations more precisely, especially for long span relations. Compared to syntax based methods, our word alignment model effectively alleviates the negative effects of parsing errors when dealing with informal online texts. In particular, compared to the traditional unsupervised alignment model, the proposed model obtains better precision because of the usage of partial supervision. In addition, when estimating candidate confidence, we penalize higherdegree vertices in our graph based co ranking algorithm to decrease the probability of error generation. Our experimental results on three corpora with different sizes and languages show that our approach effectively outperforms state of the art methods. INTRODUCTION With the rapid development of Web 2.0, a huge number of product reviews are springing up on the Web.From these reviews, customers can obtain firsthand assessments of product information and direct supervision oftheir purchase actions. Meanwhile, manufacturers canobtain immediate feedback and opportunities to improvethe quality of their products in a timely fashion. Thus, mining opinions from online reviews has become an increasingly urgent activity and has attracted a great deal ofattention from researchers. To extract and analyze opinions from online reviews, it isunsatisfactory to merely obtain the overall sentiment abouta product. In most cases, customers expect to find finegrained sentiments about an aspect or feature of a productthat is reviewed. For example:"This phone has a colorful and big screen, butits LCD resolution is very disappointing." Readers expect to know that the reviewer expresses apositive opinion of the phone's screen and a negative opinion of the screen's resolution, not just the reviewer's overallsentiment. To fulfill this aim, both opinion targets and opinion words must be detected. First, however, it is necessaryto extract and construct an opinion target list and an opinion word lexicon, both of which can provide prior knowledge that is useful for fine grained opinion miningand both of which are the focus of this paper.An opinion target is