Abstract
In the Web 2.0 era, there has been an explosive growth of user-contributed data on the Web. Among the user-contributed data, the sheer volume of online reviews (or comments) provide enterprise with invaluable market intelligence about potential customers' preferences for various products and services. However, there has been growing concerns about the quality of these uncontrolled user-contributed online reviews. Despite numerous research work has been conducted on opinion mining and opinion retrieval, little work has been done to develop effective quality metrics to assess the quality of opinionated expressions. To discover rich and accurate business intelligence from online opinionated expressions, an objective quality-based filtering process is essential for any opinion mining systems. The main contribution of this paper is the design, development, and evaluation of a novel multi-facet quality metric for the assessment of the informativeness of opinionated expressions such as online product reviews. Our preliminary experiments show that the proposed multi-facets quality metric is more effective than a quality assessment approach constructed based on user-generated helpful votes.
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