Sentiment summarization: Evaluating and learning user preferences

Sentiment summarization: Evaluating and learning user preferences (2009)

by Kevin Lerman , Sasha Blair-goldensohn , Ryan Mcdonald

Venue:	In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL
Citations:	8 - 2 self

Datum	Value	Source
TITLE	Sentiment summarization: Evaluating and learning user preferences	INFERENCE
AUTHOR NAME	Kevin Lerman	SVM HeaderParse 0.2
AUTHOR AFFIL	Columbia University; New York, NY	SVM HeaderParse 0.2
AUTHOR NAME	Sasha Blair-goldensohn	SVM HeaderParse 0.2
AUTHOR AFFIL	Google, Inc.; New York, NY	SVM HeaderParse 0.2
AUTHOR NAME	Ryan Mcdonald	SVM HeaderParse 0.2
AUTHOR AFFIL	Google, Inc.; New York, NY	SVM HeaderParse 0.2
ABSTRACT	We present the results of a large-scale, end-to-end human evaluation of various sentiment summarization models. The evaluation shows that users have a strong preference for summarizers that model sentiment over non-sentiment baselines, but have no broad overall preference between any of the sentiment-based models. However, an analysis of the human judgments suggests that there are identifiable situations where one summarizer is generally preferred over the others. We exploit this fact to build a new summarizer by training a ranking SVM model over the set of human preference judgments that were collected during the evaluation, which results in a 30 % relative reduction in error over the previous best summarizer. 1	SVM HeaderParse 0.2
YEAR	2009	INFERENCE
VENUE	In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	22 found	ParsCit 1.0