Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment

August 04-August 07

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http://doi.ieeecomputersociety.org/10.1109/ICSC.2008.67

2008 IEEE International Conference on ...

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	Adam Drake, Eric Ringger, Dan Ventura, "Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment," International Conference on Semantic Computing, pp. 152-157, 2008 IEEE International Conference on Semantic Computing, 2008.

	BibTex	x
	@article{ 10.1109/ICSC.2008.67, author = {Adam Drake and Eric Ringger and Dan Ventura}, title = {Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment}, journal ={International Conference on Semantic Computing}, volume = {0}, year = {2008}, isbn = {978-0-7695-3279-0}, pages = {152-157}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICSC.2008.67}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - International Conference on Semantic Computing TI - Sentiment Regression: Using Real-Valued Scores to Summarize Overall Document Sentiment SN - 978-0-7695-3279-0 SP152 EP157 A1 - Adam Drake, A1 - Eric Ringger, A1 - Dan Ventura, PY - 2008 KW - null VL - 0 JA - International Conference on Semantic Computing ER -

Adam Drake

Eric Ringger

Dan Ventura

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

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