Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Statistical Models for Co-occurrence Data
Statistical Models for Co-occurrence Data
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Automatically collecting, monitoring, and mining japanese weblogs
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Extraction and classification of facemarks
Proceedings of the 10th international conference on Intelligent user interfaces
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 extraction patterns for subjective expressions
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
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Just how mad are you? finding strong and weak opinion clauses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A comparative study on the use of labeled and unlabeled data for large margin classifiers
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Interactive visualization of news distribution in blog space
New Generation Computing
Evaluating brand value on the web
Proceedings of the 3rd workshop on Information credibility on the web
Multimodal Signals: Cognitive and Algorithmic Issues
Multilingual subjectivity analysis using machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Retrieval approach to extract opinions about people from resource scarce language news articles
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Collocation polarity disambiguation using web-based pseudo contexts
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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We propose to use semi-supervised learning methods to classify evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate either favorable or unfavorable opinions towards a specific subject. Due to its characteristics, the semi-supervised method that we use can classify evaluative expressions in a corpus by their polarities. This can be accomplished starting from a very small set of seed training examples and using contextual information in the sentences to which the expressions belong. Our experimental results with actual Weblog data show that this bootstrapping approach can improve the accuracy of methods for classifying favorable and unfavorable opinions.