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Text Representation Using Dependency Tree Subgraphs for Sentiment Analysis

  • Alexander Pak
  • Patrick Paroubek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6637)

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.

Keywords

Opinion Mining Sentiment Analysis Text Representation Dependency Tree Discount Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alexander Pak
    • 1
  • Patrick Paroubek
    • 1
  1. 1.Laboratoire LIMSI-CNRSUniversité de Paris-SudOrsayFrance

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