Automatically Determining Review Helpfulness
نویسندگان
چکیده
Customer reviews from commerce websites have valuable information for online shoppers. They help shoppers gauge whether or not a product is worth the purchase. However, reviews vary in their quality and helpfulness. Most commerce websites have voting systems where shoppers can vote on whether a review was helpful to them or not. For popular products however, the number of reviews can be in the thousands. As a result, not all reviews will get enough attention to receive helpfulness votes even though some may contain helpful information for other shoppers. In these scenarios, it would be desirable to be able to automatically collect the most helpful reviews. This research aims to do this by finding features in the review text that are indicative of its helpfulness and training a learning algorithm that can determine review helpfulness.
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