Opinion Targets and Opinion Words Co-Extraction from Online Reviews Based on the Novel Approach with Partially Supervised Alignment Model
Keywords:
Opinion mining, opinion targets extraction, opinion words extractionAbstract
One of the most significant tasks of opinion mining is mining opinion targets and opinion words from the online reviews. The major key component is to detect opinion relations between words. This paper proposes a novel approach based on the partially-supervised alignment model which identifies opinion relations as an alignment process. There after a graph-based co-ranking algorithm is made used, in order to estimate the confidence of each candidate. Lastly, candidates having higher confidence are extracted as the opinion targets or the opinion words. When compared with the other methods, this model is making the task of opinion relations for long-span relations. When compared to the syntax-based methods, our word alignment model effectively reduces the negative effects of the parsing errors when dealing with the informal online texts. So, when compared to the traditional unsupervised alignment model, the proposed model obtains better precision because of the usage of partial supervision. When estimating the candidate confidence, we got to know that higher-degree vertices in our graph-based co-ranking algorithm to decrease the probability of generation of error.
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