With the emergence of Web 2.0, user generated content in the form of online product reviews has proliferated. Although product reviews contain valuable information, they vary greatly in terms of quality and credibility. This study presents an opinion mining framework - Cred-OPMiner (Credibility-Specific-Opinion Miner) - by combining the concepts of credibility and aspect based opinion mining. Cred-OPMiner performs three main tasks. The first task is to group reviewers based on the credibility dimensions. The second critical task is aspect extraction in which aspects of a given product are identified using a novel hybrid and domain independent algorithm. The final task is the sentiment prediction task where the sentiment on each aspect is computed. The key novelty is utilizing source credibility concepts for online reviewer clustering. Source credibility dimensions including trustworthiness and expertise are quantified using reviewers’ data. In addition, a new aspect extraction technique is developed and incorporated in the Cred-OPMiner. Cred-OPMiner was tested using data crawled from epinions.com. It groups reviewers and then performs aspect based opinion mining by differentiating among opinions of various reviewer groups.
