Abstract
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.
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Tu, Z., Jiang, W., Liu, Q., Lin, S. (2012). Dependency Forest for Sentiment Analysis. In: Zhou, M., Zhou, G., Zhao, D., Liu, Q., Zou, L. (eds) Natural Language Processing and Chinese Computing. NLPCC 2012. Communications in Computer and Information Science, vol 333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34456-5_7
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DOI: https://doi.org/10.1007/978-3-642-34456-5_7
Publisher Name: Springer, Berlin, Heidelberg
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