Volume : 3, Issue : 4, APR 2017

OPINION EXTRACTION FROM ONLINE REVIEWS

S.VIDHYA, C.VIDHYA ABINAYA, R.VISUTHA, P.V.KAVITHA

Abstract

Now a days online shopping is mostly preferred by customers rather than going to the shops. So before they buy a product, they will refer to the previous customer’s reviews by navigating to the review page. Opinion mining eases this task by classifying the customer reviews. This paper proposes a novel approach based on the partially supervised word alignment model to extract the opinion relations using constrained Hill Climbing Algorithm. Finally, using these relations, the number of positive reviews and the number of negative reviews are classified. Compared to previous method which is based on the nearest neighbor rules that cannot support large sized corpus, our model captures opinion relations accurately, especially for long sized corpus. Compared to Syntax – based method, our model effectively ignores the parsing errors when dealing with informal text.

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References

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