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Full-Text Articles in Computer Sciences
Sentiment Regression: Using Real-Valued Scores To Summarize Overall Document Sentiment, Adam Drake, Eric K. Ringger, Dan A. Ventura
Sentiment Regression: Using Real-Valued Scores To Summarize Overall Document Sentiment, Adam Drake, Eric K. Ringger, Dan A. Ventura
Faculty Publications
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).
Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting
Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting
Faculty Publications
Classification problems with output class overlap create problems for standard neural network approaches. We present a modification of a simple feed-forward neural network that is capable of learning problems with output overlap, including problems exhibiting hierarchical class structures in the output. Our method of applying weakened implicit negatives to address overlap and ambiguity allows the algorithm to learn a large portion of the hierarchical structure from very incomplete data. Our results show an improvement of approximately 58% over a standard backpropagation network on the hierarchical problem.