Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
A Lexicon-Enhanced Method for Sentiment Classification: An Experiment on Online Product Reviews
IEEE Intelligent Systems
A hybrid approach to emotional sentence polarity and intensity classification
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Lexicon-based methods for sentiment analysis
Computational Linguistics
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Traditional sentiment feature extraction methods in document-level sentiment classification either count the frequencies of sentiment words as features, or the frequencies of modified and unmodified instances of each of these words. However, these methods do not represent the sentiment words' linguistic context efficiently. We propose a novel method and feature set to handle the contextual polarity of sentiment words efficiently. Our experiments on both movie and product reviews show a significant improvement in the classifier's performance (an overall accuracy increase of 2%), in addition to statistical significance of our feature set over the traditional feature set. Also, compared with other widely-used feature sets, most of our features are among the key features for sentiment classification.