Dependency Forest for Sentiment Analysis

摘要

Dependency Grammars prove to be effective in improving sentiment analysis,because they can directly capture syntactic relations between words.However,most dependency-based systems suffer from a major drawback: they only use 1-best dependency trees for feature extraction,which adversely affects the performance due to parsing errors.Therefore,we propose an approach that applies dependency forest to sentiment analysis.A dependency forest compactly represents multiple dependency trees.We develop new algorithms for extracting features from dependency forest.Experiments show that our forest-based system obtains 5.4 point absolute improvement in accuracy over a bag-of-words system,and 1.3 point improvement over a tree-based system on a widely used sentiment dataset.Our forest-based system also achieves state-ofthe- art performance on the sentiment dataset.