UCD Computer Scientists Develop Technique to Improve Helpfulness of User-Generated Online Review
From the ACM Tech News Summary:
University College Dublin (UCD) researchers have developed a system to rate the helpfulness of online customer reviews. Some online services currently allow users to rate the helpfulness of a review, but the feedback is minimal and can be biased and poorly written. The new system applies machine learning to determine the factors that contribute to a helpful review. "It's about identifying which reviews are the most informative about the product or service," says UCD's Michael O'Mahony. The system uses four basic factors in determining the helpfulness of a review--reputation, social, sentiment, and content. Reputation analyzes all previous reviews written by the same author, determining if the author is known for providing useful comments. Social accounts for how often reviewers respond to other reviews, improving the quality of their own statements about products or services. Sentiment acknowledges that users tend to respond more to positive reviews, and takes the score attributed to a product into account. Content looks at how well the review is written by form and length because poorly written reviews are considered less helpful to users. "One interesting result that we noticed is that users tend to be drawn more towards positive reviews and often ignore negative reviews, even though the negative reviews can be very revealing and informative," according to O'Mahony. He says the new system can be used to promote helpful negative reviews that might otherwise be overlooked by users.
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