Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
UPAR7: a knowledge-based system for headline sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis
ECIR'07 Proceedings of the 29th European conference on IR research
Dependency tree-based sentiment classification using CRFs with hidden variables
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A study of information retrieval weighting schemes for sentiment analysis
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Last but definitely not least: on the role of the last sentence in automatic polarity-classification
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Sentiment classification using automatically extracted subgraph features
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
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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.