IEEE Transactions on Pattern Analysis and Machine Intelligence
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A perspective view and survey of meta-learning
Artificial Intelligence Review
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
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
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Multi-domain sentiment classification
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Co-training for cross-lingual sentiment classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
A framework of feature selection methods for text categorization
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Mine the easy, classify the hard: a semi-supervised approach to automatic sentiment classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Imbalanced sentiment classification
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning opinions in user-generated web content
Natural Language Engineering
Buy it - don't buy it: sentiment classification on amazon reviews using sentence polarity shift
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Active learning on sentiment classification by selecting both words and documents
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
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Polarity shifting marked by various linguistic structures has been a challenge to automatic sentiment classification. In this paper, we propose a machine learning approach to incorporate polarity shifting information into a document-level sentiment classification system. First, a feature selection method is adopted to automatically generate the training data for a binary classifier on polarity shifting detection of sentences. Then, by using the obtained binary classifier, each document in the original polarity classification training data is split into two partitions, polarity-shifted and polarity-unshifted, which are used to train two base classifiers respectively for further classifier combination. The experimental results across four different domains demonstrate the effectiveness of our approach.