Utility Scoring of Product Reviews. Zhu Zhang Department of Management Information System University of Arizona Balaji Varadarajan Department of Computer Science University of Arizona CIKM’06. Outline. Introduction Problem Definition Data Collection
Department of Management Information System University of Arizona
Department of Computer Science University of Arizona
T: product review : the real utility scores : the predicted utility scores
and : the mean of the corresponding sample respectively
4.1 Learning Algorithms
 I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques.
Morgan Kaufmann, San Francisco, 2005.
 V. Hatzivassiloglou and J. M. Wiebe. Effects of adjective orientation and gradability on sentence subjectivity. In Proceedings of the 18th conference on Computational linguistics, pages 299–305, Morristown, NJ, USA, 2000. Association for Computational Linguistics.
 J. Wiebe. Learning subjective adjectives from corpora. In AAAI/IAAI, pages 735–740, 2000.
 E. Riloff, J. Wiebe, and T. Wilson. Learning subjective nouns using extraction pattern bootstrapping. In W. Daelemans and M. Osborne, editors, Proceedings of CoNLL-2003, pages 25–32. Edmonton, Canada, 2003.
 M. Thelen and E. Riloff. A bootstrapping method for learning semantic lexicons using extraction pattern contexts. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 214–221, Philadelphia, July 2002. Association for Computational Linguistics.
5.1 Data Treatment
5.2 Experimental Results
> 0.30 and < 0.10, and apparently outperforms the length-based baseline