Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
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
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Hierarchical sequential learning for extracting opinions and their attributes
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Structure-aware review mining and summarization
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Sentence based sentiment classification from online customer reviews
Proceedings of the 8th International Conference on Frontiers of Information Technology
ELS: a word-level method for entity-level sentiment analysis
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
An unsupervised sentiment classifier on summarized or full reviews
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Self-training from labeled features for sentiment analysis
Information Processing and Management: an International Journal
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
Comparison of feature-level learning methods for mining online consumer reviews
Expert Systems with Applications: An International Journal
Generating syntactic tree templates for feature-based opinion mining
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Incorporating Sentiment Prior Knowledge for Weakly Supervised Sentiment Analysis
ACM Transactions on Asian Language Information Processing (TALIP)
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In this paper, we present a novel method based on CRF's in response to the two special characteristics of "contextual dependency" and "label redundancy" in sentence sentiment classification. We try to capture the contextual constraints on sentence sentiment using CRFs. Through introducing redundant labels into the original sentimental label set and organizing all labels into a hierarchy, our method can add redundant features into training for capturing the label redundancy. The experimental results prove that our method outperforms the traditional methods like NB, SVM, MaxEnt and standard chain CRFs. In comparison with the cascaded model, our method can effectively alleviate the error propagation among different layers and obtain better performance in each layer.