  Due to flourish of the Web 2.0, web opinion sources are rapidly emerging
containing precious information useful for both customers and manufactures.
Recently, feature based opinion mining techniques are gaining momentum in which
customer reviews are processed automatically for mining product features and
user opinions expressed over them. However, customer reviews may contain both
opinionated and factual sentences. Distillations of factual contents improve
mining performance by preventing noisy and irrelevant extraction. In this
paper, combination of both supervised machine learning and rule-based
approaches are proposed for mining feasible feature-opinion pairs from
subjective review sentences. In the first phase of the proposed approach, a
supervised machine learning technique is applied for classifying subjective and
objective sentences from customer reviews. In the next phase, a rule based
method is implemented which applies linguistic and semantic analysis of texts
to mine feasible feature-opinion pairs from subjective sentences retained after
the first phase. The effectiveness of the proposed methods is established
through experimentation over customer reviews on different electronic products.
