A standard approach for supervised sentiment analysis with n-grams features cannot correctly identify complex sentiment expressions due to the loss of information when representing a text using the bag-of-words model. In our research, we propose to use subgraphs from the dependency tree of a parsed sentence as features for sentiment classification. We represent a text with a feature vector based on extracted subgraphs and use state of the art SVM classifier to identify the polarity of the given text. Our experimental evaluations on the movie-review dataset show that using our proposed features outperforms the standard bag-of-words and n-gram models. In this paper, we work with English, however most of our techniques can be easily adapted for other languages.
CITATION STYLE
Pak, A., & Paroubek, P. (2011). Text representation using dependency tree subgraphs for sentiment analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6637 LNCS, pp. 323–332). Springer Verlag. https://doi.org/10.1007/978-3-642-20244-5_31
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