OPINION BASED WORD EXTRACTION OF SUPERVISED & UNSUPERVISED MODELS FOR ALIGNMENT

Shalaka Gaidhani, Dr M. U. Kharat

Abstract


Extracting opinion targets and opinion words from online reviews are two fundamental tasks in opinion mining. 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. The proposed model captures opinion relations more precisely, especially for long-span relations. It 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, it penalizes higher-degree vertices in graph-based co-ranking algorithm to decrease the probability of error generation.

 

Keywords: Opinion targets, opinion words, alignment models.


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DOI: https://doi.org/10.26483/ijarcs.v6i8.2601

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