Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
A hierarchical Bayesian language model based on Pitman-Yor processes
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
Latent aspect rating analysis on review text data: a rating regression approach
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An unsupervised aspect-sentiment model for online reviews
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Trend analysis model: trend consists of temporal words, topics, and timestamps
Proceedings of the fourth ACM international conference on Web search and data mining
Predicting future reviews: sentiment analysis models for collaborative filtering
Proceedings of the fourth ACM international conference on Web search and data mining
Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models
The Journal of Machine Learning Research
Identifying sentiments over N-gram
Proceedings of the 21st international conference companion on World Wide Web
A phrase-discovering topic model using hierarchical Pitman-Yor processes
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 a Bayesian nonparametric topic model that rep- resents relationships between given labels and the corre- sponding words/phrases, from supervised articles. Unlike existing supervised topic models, our proposal, supervised N-gram topic model (SNT), focuses on both a number of topics and power-law distribution in the word frequencies to extract topic specific N-grams. To achieve this goal, SNT takes a Bayesian nonparametric approach to topic sampling, which generates word distribution jointly with the given variable in textual order, and then form each N-gram word as a hierarchy of Pitman-Yor process priors. Experiments on labeled text data show that SNT is useful as a generative model for discovering more phrases that complement human experts and domain specific knowledge than the existing al- ternatives. The results show that SNT can be applied to various tasks such as automatic annotation.