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-of-the-art performance on the sentiment dataset. © 2012 Springer-Verlag.
CITATION STYLE
Tu, Z., Jiang, W., Liu, Q., & Lin, S. (2012). Dependency forest for sentiment analysis. In Communications in Computer and Information Science (Vol. 333 CCIS, pp. 69–77). https://doi.org/10.1007/978-3-642-34456-5_7
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