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RevRank: a Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews

This video was recorded at 3rd International AAAI Conference on Weblogs and Social Media (ICWSM), San Jose 2009. 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.

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science / Programming & Programming Languages

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Material Type:: Presentation
Date Added to MERLOT:: February 8, 2015
Date Modified in MERLOT:: February 8, 2015
Author:: Oren Tsur, Harvard School of Engineering and Applied Sciences, Harvard University
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Accessibility Information Available:: Unknown
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States