Gregory Grefenstette, Yan Qu, David A. Evans, and James G. Shanahan
Today, much of product feedback is provided by customers/critiques online through websites, discussion boards, mailing lists, and blogs. People trying to make strategic decisions (e.g., a product launch, a purchase) will find that a web search will return many useful but heterogeneous and, increasingly, multilingual opinions on a product. Generally, the user will find it very difficult and time consuming to assimilate all available information and make an informed decision. To date, most work in automating this process has focused on monolingual texts and users. This extended abstract describes our preliminary work on mining product ratings in a multilingual setting. The proposed approaches are automatic, using a combination of techniques from classification and translation, thereby alleviating human-intensive construction and maintenance of linguistic resources.