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Due to flourish of the Web 2.0, web opinion sources are rapidly emergingcontaining precious information useful for both customers and manufactures.Recently, feature based opinion mining techniques are gaining momentum in whichcustomer reviews are processed automatically for mining product features anduser opinions expressed over them. However, customer reviews may contain bothopinionated and factual sentences. Distillations of factual contents improvemining performance by preventing noisy and irrelevant extraction. In thispaper, combination of both supervised machine learning and rule-basedapproaches are proposed for mining feasible feature-opinion pairs fromsubjective review sentences. In the first phase of the proposed approach, asupervised machine learning technique is applied for classifying subjective andobjective sentences from customer reviews. In the next phase, a rule basedmethod is implemented which applies linguistic and semantic analysis of textsto mine feasible feature-opinion pairs from subjective sentences retained afterthe first phase. The effectiveness of the proposed methods is establishedthrough experimentation over customer reviews on different electronic products.

Subjectivity Classification using Machine Learning Techniques for Mining Feature-Opinion Pairs from Web Opinion Sources