The rapidly growing popularity and activity of Web communities raises novel questions of appropriate aggregation and diversification of such social contents. In many cases, users are interested in gaining an extensive overview over pros and cons of a particular track of contributions.
We address the problem of social content diversification by combining latent semantic analysis with feature-centric sentiment analysis. Our FREuD approach provides a representative overview of sub-topics and aspects of discussions, characteristic user sentiments under different aspects, and reasons expressed by different opponents.
The data set used in our FREuD experiments is provided below for further research with appropirate citation to the paper mentioned at the end of this page in the Literature section. This data set consists of end user product reviews crawled from CNET product review portal for popular products in "Cellphone", "Digital Camera" and "Printer" categories. Each review has been annotated by three unique annotators for the product features discussed in the review and associated sentiment of the end user towards the feature.
|Product Category||No. of Products||No. of Reviews||No. of Features||Data Files|
|Digital Cameras||6||150||11||ReviewText.xml, Annotations.xml|