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
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Sentiment analysis of blogs by combining lexical knowledge with text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic seed word selection for unsupervised sentiment classification of Chinese text
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SELC: a self-supervised model for sentiment classification
Proceedings of the 18th ACM conference on Information and knowledge management
Sentiment analysis of Chinese documents: From sentence to document level
Journal of the American Society for Information Science and Technology
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
Handbook of Natural Language Processing
Handbook of Natural Language Processing
Employing personal/impersonal views in supervised and semi-supervised sentiment classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Aspect-based sentiment analysis of movie reviews on discussion boards
Journal of Information Science
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
Selecting Attributes for Sentiment Classification Using Feature Relation Networks
IEEE Transactions on Knowledge and Data Engineering
Exploiting effective features for chinese sentiment classification
Expert Systems with Applications: An International Journal
Self-training from labeled features for sentiment analysis
Information Processing and Management: an International Journal
Bilingual co-training for sentiment classification of chinese product reviews
Computational Linguistics
Creating subjective and objective sentence classifiers from unannotated texts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Semi-supervised learning for imbalanced sentiment classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Learning to identify review spam
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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With the rapid evolution of documents on the World Wide Web which express opinions, there exists an increasing demand for developing such a sentiment analysis technique that can easily adapt to new domains with minimum supervision. This article introduces a novel weakly supervised approach for Chinese sentiment classification. The approach applies a variant of self-training algorithm on two partitions split from test dataset, and combines classification results of the two partitions into a pseudo-labelled training set and an unlabelled test set, then trains an initial classifier on the pseudo-labelled training set and adopts a standard self-learning cycle to obtain the overall classification results. Experiments on the four datasets from two domains show that our approach has competitive advantages over baseline approaches; it even outperforms the supervised approach in some of the datasets despite using no labelled documents.