RevRank: a Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews
author: Oren Tsur,
Harvard School of Engineering and Applied Sciences, Harvard University
published: June 24, 2009, recorded: May 2009, views: 5059
published: June 24, 2009, recorded: May 2009, views: 5059
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
We present an algorithm for automatically ranking usergenerated book reviews according to review helpfulness. Given a collection of reviews, our REVRANK algorithm identifies a lexicon of dominant terms that constitutes the core of a virtual optimal review. This lexicon defines a feature vector representation. Reviews are then converted to this representation and ranked according to their distance from a ‘virtual core’ review vector. The algorithm is fully unsupervised and thus avoids costly and error-prone manual training annotations. Our experiments show that REVRANK clearly outperforms a baseline imitating the Amazon user vote review ranking system.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: