Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment Model

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Name Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment

Model

Technology Dot net, MS SQL
Category Data Mining
Description 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 nearest-neighbor rules, our

model captures opinion relations more precisely,
especially for long-span relations. Compared to syntaxbased 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 higher -degree 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.

IEEE Paper Yes
IEEE Paper Year 2014
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