sentiment regression, classification, real-valued score
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). All Faculty Publications. 904.
Physical and Mathematical Sciences
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