An Efficient Approach for Co-Extracting Opinion Targets Based on Online Reviews Using Supervised Word Alignment Model

( Vol-2,Issue-12,December 2016 )

Author(s): T.Gopi Chand, Chinta Someswararao

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Page No: 2028-2031
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Keywords:

Opinion mining, opinion targets extraction, opinion words extraction.

Abstract:

Mining opinion targets and opinion words from on-line reviews square measure vital tasks for fine-grained opinion mining, the key part of that involves detective work opinion relations among words.We propose An Efficient Approach for Co-Extracting Opinion Targets in Online Reviews Based on Supervised Word-Alignment Model it is a unique approach supported the fully-supervised alignment model that regards distinctive opinion relations as an alignment method. Then, a graph-based Re-ranking algorithmic rule is exploited to estimate the boldness of every candidate. This Re-ranking algorithm is used to achieve the better results from co-extracting word alignment model. Finally, candidates with higher confidence area unit extracted as opinion targets or opinion words. Compared to previous ways supported the nearest-neighbor rules, our model captures opinion relations a lot of exactly, particularly for long-span relations. Compared to syntax-based ways, our word alignment model effectively alleviates the negative effects of parsing errors once managing informal on-line texts. In explicit, compared to the normal unsupervised alignment model, the planned model obtains higher exactness attributable to the usage of fully oversight. Additionally, once estimating candidate confidence, we have a tendency to punish higher-degree vertices in our graph-based Re-ranking algorithmic rule to decrease the likelihood of error generation. Our experimental results on 3 corpora with completely different sizes and languages show that our approach effectively outperforms progressive ways.

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