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
Learning sentiment classification model from labeled features
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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
Latent sentiment model for weakly-supervised cross-lingual sentiment classification
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Feature subsumption for sentiment classification in multiple languages
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Incorporating Sentiment Prior Knowledge for Weakly Supervised Sentiment Analysis
ACM Transactions on Asian Language Information Processing (TALIP)
A weakly supervised approach to Chinese sentiment classification using partitioned self-training
Journal of Information Science
Information Technology and Management
Bootstrapping polarity classifiers with rule-based classification
Language Resources and Evaluation
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In this work, we propose a novel scheme for sentiment classification (without labeled examples) which combines the strengths of both "learn-based" and "lexicon-based" approaches as follows: we first use a lexicon-based technique to label a portion of informative examples from given task (or domain); then learn a new supervised classifier based on these labeled ones; finally apply this classifier to the task. The experimental results indicate that proposed scheme could dramatically outperform "learn-based" and "lexicon-based" techniques.