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Mining the Web: Discovering Knowledge from HyperText Data
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Information Theory, Inference & Learning Algorithms
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Thumbs up?: sentiment classification using machine learning techniques
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Recognizing contextual polarity in phrase-level sentiment analysis
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Automatic construction of polarity-tagged corpus from HTML documents
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Opinion Mining and Sentiment Analysis
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Extracting Subjective and Objective Evaluative Expressions from the Web
ISUC '08 Proceedings of the 2008 Second International Symposium on Universal Communication
Modeling latent-dynamic in shallow parsing: a latent conditional model with improved inference
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Learning with compositional semantics as structural inference for subsentential sentiment analysis
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The effect of negation on sentiment analysis and retrieval effectiveness
Proceedings of the 18th ACM conference on Information and knowledge management
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
Multi-level structured models for document-level sentiment classification
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Affect analysis model: Novel rule-based approach to affect sensing from text
Natural Language Engineering
ELS: a word-level method for entity-level sentiment analysis
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Text representation using dependency tree subgraphs for sentiment analysis
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Discovering fine-grained sentiment with latent variable structured prediction models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Sentiment analysis of citations using sentence structure-based features
HLT-SS '11 Proceedings of the ACL 2011 Student Session
Joint bilingual sentiment classification with unlabeled parallel corpora
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Semi-supervised latent variable models for sentence-level sentiment analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Imbalanced sentiment classification
Proceedings of the 20th ACM international conference on Information and knowledge management
Semi-supervised recursive autoencoders for predicting sentiment distributions
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Compositional matrix-space models for sentiment analysis
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Training dependency parsers by jointly optimizing multiple objectives
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Entity-centric topic-oriented opinion summarization in twitter
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Grammatical structures for word-level sentiment detection
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Baselines and bigrams: simple, good sentiment and topic classification
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Why question answering using sentiment analysis and word classes
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Semantic compositionality through recursive matrix-vector spaces
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
WikiSent: weakly supervised sentiment analysis through extractive summarization with wikipedia
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Sentiment profiles of multiword expressions in test-taker essays: The case of noun-noun compounds
ACM Transactions on Speech and Language Processing (TSLP) - Special issue on multiword expressions: From theory to practice and use, part 2
Rule-based opinion target and aspect extraction to acquire affective knowledge
Proceedings of the 22nd international conference on World Wide Web companion
Emotion detection in suicide notes
Expert Systems with Applications: An International Journal
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In this paper, we present a dependency tree-based method for sentiment classification of Japanese and English subjective sentences using conditional random fields with hidden variables. Subjective sentences often contain words which reverse the sentiment polarities of other words. Therefore, interactions between words need to be considered in sentiment classification, which is difficult to be handled with simple bag-of-words approaches, and the syntactic dependency structures of subjective sentences are exploited in our method. In the method, the sentiment polarity of each dependency subtree in a sentence, which is not observable in training data, is represented by a hidden variable. The polarity of the whole sentence is calculated in consideration of interactions between the hidden variables. Sum-product belief propagation is used for inference. Experimental results of sentiment classification for Japanese and English subjective sentences showed that the method performs better than other methods based on bag-of-features.