Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
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
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
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
A novel scheme for domain-transfer problem in the context of sentiment analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Comparative experiments on sentiment classification for online product reviews
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Automatic seed word selection for unsupervised sentiment classification of Chinese text
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
SemEval-2010 task 18: Disambiguating sentiment ambiguous adjectives
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Learning sentiment classification model from labeled features
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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
Incorporating Sentiment Prior Knowledge for Weakly Supervised Sentiment Analysis
ACM Transactions on Asian Language Information Processing (TALIP)
Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media
ACM Transactions on Intelligent Systems and Technology (TIST)
Transverse subjectivity classification
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Generating virtual ratings from chinese reviews to augment online recommendations
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
A weakly supervised approach to Chinese sentiment classification using partitioned self-training
Journal of Information Science
SemEval-2010 task 18: disambiguating sentiment ambiguous adjectives
Language Resources and Evaluation
Bootstrapping polarity classifiers with rule-based classification
Language Resources and Evaluation
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This paper presents the SELC Model (SElf-Supervised, (Lexicon-based and (Corpus-based Model) for sentiment classification. The SELC Model includes two phases. The first phase is a lexicon-based iterative process. In this phase, some reviews are initially classified based on a sentiment dictionary. Then more reviews are classified through an iterative process with a negative/positive ratio control. In the second phase, a supervised classifier is learned by taking some reviews classified in the first phase as training data. Then the supervised classifier applies on other reviews to revise the results produced in the first phase. Experiments show the effectiveness of the proposed model. SELC totally achieves 6.63% F1-score improvement over the best result in previous studies on the same data (from 82.72% to 89.35%). The first phase of the SELC Model independently achieves 5.90% improvement (from 82.72% to 88.62%). Moreover, the standard deviation of F1-scores is reduced, which shows that the SELC Model could be more suitable for domain-independent sentiment classification.