2008 IEEE International Conference on Semantic Computing
Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment
August 04-August 07
ISBN: 978-0-7695-3279-0
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).
Citation:
Adam Drake, Eric Ringger, Dan Ventura, "Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment," icsc, pp.152-157, 2008 IEEE International Conference on Semantic Computing, 2008