Keywords
sentiment regression, classification, real-valued score
Abstract
In this paper, we consider a sentiment regression problem: summarizing the overall sentiment of a review with a real-valued score. Empirical results on a set of labeled reviews show that real-valued sentiment modeling is feasible, as several algorithms improve upon baseline performance. We also analyze performance as the granularity of the classification problem moves from two-class (positive vs. negative) towards infinite-class (real-valued).
Original Publication Citation
Adam Drake, Eric Ringger and Dan Ventura, "Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment", Proceedings of the IEEE International Conference on Semantic Computing, pp. 152-157, 28.
BYU ScholarsArchive Citation
Drake, Adam; Ringger, Eric K.; and Ventura, Dan A., "Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment" (2008). Faculty Publications. 904.
https://scholarsarchive.byu.edu/facpub/904
Document Type
Peer-Reviewed Article
Publication Date
2008-01-01
Permanent URL
http://hdl.lib.byu.edu/1877/2534
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/