Abstract
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.
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Pak, A., Paroubek, P. (2011). Text Representation Using Dependency Tree Subgraphs for Sentiment Analysis. In: Xu, J., Yu, G., Zhou, S., Unland, R. (eds) Database Systems for Adanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20244-5_31
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DOI: https://doi.org/10.1007/978-3-642-20244-5_31
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