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In this paper, we have planned to propose a complete unique technique to spot opinion features from on-line reviews by exploiting the two distinct opinion feature statistics across two corpora, one domain-specific corpus (i.e., the given review corpus) and one domain-independent corpus (i.e., the contrastive corpus).We capture this inequality via domain connection (DR) that characterizes the connection of a term to a text assortment. Initial list extraction of candidate opinion options is done from domain review corpus by following the grammar dependence rules. For every extracted candidate feature, we can estimate its intrinsic-domain connection (IDR) and extrinsic-domain connection (EDR) scores on the domain-dependent and domain-independent corpora, severally. The aim of document-level (sentence-level) opinion mining is to classify the general judgment or sentiment expressed in a personal review document. Thus, on the basis of candidate feature, the interval threshold can be used for intrinsic and extrinsic domain connection criterion. Evaluations conducted on two real-world review domains demonstrate the effectiveness of our projected IEDR approach in distinguishing opinion options.

Review Paper on Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance