Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
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
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
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
Journal of the American Society for Information Science and Technology
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
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and 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
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
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
MIEA: a mutual iterative enhancement approach for cross-domain sentiment classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Sentic Computing: Techniques, Tools, and Applications
Sentic Computing: Techniques, Tools, and Applications
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In this paper, we consider the problem of building models that have high subjectivity classification accuracy across domains. For that purpose, we present and evaluate new methods based on multi-view learning using both high-level (i.e. linguistic features for subjectivity detection) and low-level features (i.e. unigrams and bigrams). In particular, we show that multi-view learning, combining high-level and low-level features with adapted classifiers, can lead to improved results compared to one of the state-of-the-art algorithms called Stochastic Agreement Regularization. In particular, the experiments show that dividing the set of characteristics into three views returns the best results overall with accuracy across domains of 91.3% for the Class-Guided Multi-View Learning Algorithm, which combines both Linear Discriminant Analysis and Support Vector Machines.