Ruihai Dong, Michael P. O'Mahony, Kevin McCarthy
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User-generated reviews are now plentiful online and they have proven to be a valuable source of real user opinions and real user experiences. In this chapter we consider recent work that seeks to extract topics, opinions, and sentiment from review text that is unstructured and often noisy. We describe and evaluate a number of practical case-studies for how such information can be used in an information filtering and recommendation context, from filtering helpful reviews to recommending useful products.